Things I would have told myself before building an autorouter(blog.autorouting.com)
blog.autorouting.com
Things I would have told myself before building an autorouter
https://blog.autorouting.com/p/13-things-i-would-have-told-myself
30 comments
We will absolutely be supporting KiCad! Placement is something we have big plans for but I think it’s important to have a really fast, cache-friendly autorouter as a foundation (if you are cache friendly, moving components and trying different layouts is much much faster)
Javascript is fairly portable now with many runtimes (some even quite small such as QuickJS or Proffor), so I anticipate people will be able to run locally and build their own ginormous cache :)
I think everyone should be concerned about lockin and fragmenting ecosystems in EDA, but tscircuit and our autorouter are MIT-permissive technology (very rare in EDA) so we play nice (interoperable) with everyone.
Javascript is fairly portable now with many runtimes (some even quite small such as QuickJS or Proffor), so I anticipate people will be able to run locally and build their own ginormous cache :)
I think everyone should be concerned about lockin and fragmenting ecosystems in EDA, but tscircuit and our autorouter are MIT-permissive technology (very rare in EDA) so we play nice (interoperable) with everyone.
> We will absolutely be supporting KiCad
I wonder why there's no standard API for autorouting so that a particular EDA must be supported. I'd love to see autorouter as a standardized HTTP endpoint so that I can run one locally or even buy Autorouter-As-A-Service. E.g. I'd happily pay for TopoR¹ as a service for some projects while others are fine with classic rouing.
¹) https://de.wikipedia.org/wiki/TopoR
I wonder why there's no standard API for autorouting so that a particular EDA must be supported. I'd love to see autorouter as a standardized HTTP endpoint so that I can run one locally or even buy Autorouter-As-A-Service. E.g. I'd happily pay for TopoR¹ as a service for some projects while others are fine with classic rouing.
¹) https://de.wikipedia.org/wiki/TopoR
Software innovation in EDA seems pretty slow and is more IP-sensitive, so there hasn't been a huge proliferation of cloud services. We're trying to bring some up some standards that make EDA more web-friendly, like the Circuit JSON[1] format and the Simple Route JSON format[2], but it'll still be years before the tools support web-first standards (kind of reminds me of png vs webp)
[1] https://github.com/tscircuit/circuit-json
[2] https://docs.tscircuit.com/advanced/simple-route-json
[1] https://github.com/tscircuit/circuit-json
[2] https://docs.tscircuit.com/advanced/simple-route-json
How are you planning to support kicad? Would it be using kicad's new IPC API? In principle it should be possible to create javascript bindings for the new protobuf interface.
We are looking hard at the IPC interface or doing a plugin, but we will also support “uploading” kicad_sch/kicad_pcb to a browser (local-first, so not actually uploading)
Hey - take a look at my other reply to u/ChrisGammell - I have some input on your project you might find useful that I don’t feel like retyping lol
Years ago the long-gone and unlamented OrCAD Layout had a spreadsheet view of your nets that was a slightly better than good-enough interface for setting constraints for autorouting. Once you got your footprints, placement, constraints, and hand-routed nets locked down, you could iterate very quickly.
It's nice to see anyone at all working on PCB autorouters, which have been pretty stagnant since Cadence bought SPECCTRA in the nineties. The guys who wrote SPECCTRA went to the VLSI world iirc and never came back - I guess that's where all the fame and fortune is. Probably was a patent minefield for a while there too; might still be. Autoplacement was an utterly intractable problem back then, still seems to be, and it's probably ripe for a generative AI approach - a good generative AI first pass at parts placement could be a net time saver. The biggest problem would be convincing the cranks that you can be good even if you can't be perfect.
I'm sort of puzzled by the kids out there trying to do schematics-as-code. On the one hand, I would love to see that work as a backend format - especially where folks like the jitx guys seem to be making a lot of progress in encoding appnote and datasheet-level design rules into parts models. Reading every datasheet in the detail needed for commercial designs is more work than people expect, and so is getting junior engineers on board with the process. So any automation there is beneficial. The problem is that the approaches all seem rooted in this idea of schematics as data entry for layout, a kind of source code, rather than as design documentation with a carefully evolved visual language that needs to be accessible to people who won't have an EDA suite installed on their computer. I guess people who learned from puzzling out schematics in the Adafruit/Sparkfun/Shenzhen style with explicit wiring minimized might not understand the value of a good schematic.
The other thing is leaning too far into thinking-by-analogy and trying to make PCB-level design like VLSI design. I don't think it's entirely impossible - if we had better tools for DRC and validation, component-level design would look more like VLSI design. But the design space is so different, with such loose coupling between design, EDA/CAM/simulation, validation, fabricators, assemblers, component vendors, regulators/certifiers, and so on that just getting one corner of it really right would be a major accomplishment.
It's nice to see anyone at all working on PCB autorouters, which have been pretty stagnant since Cadence bought SPECCTRA in the nineties. The guys who wrote SPECCTRA went to the VLSI world iirc and never came back - I guess that's where all the fame and fortune is. Probably was a patent minefield for a while there too; might still be. Autoplacement was an utterly intractable problem back then, still seems to be, and it's probably ripe for a generative AI approach - a good generative AI first pass at parts placement could be a net time saver. The biggest problem would be convincing the cranks that you can be good even if you can't be perfect.
I'm sort of puzzled by the kids out there trying to do schematics-as-code. On the one hand, I would love to see that work as a backend format - especially where folks like the jitx guys seem to be making a lot of progress in encoding appnote and datasheet-level design rules into parts models. Reading every datasheet in the detail needed for commercial designs is more work than people expect, and so is getting junior engineers on board with the process. So any automation there is beneficial. The problem is that the approaches all seem rooted in this idea of schematics as data entry for layout, a kind of source code, rather than as design documentation with a carefully evolved visual language that needs to be accessible to people who won't have an EDA suite installed on their computer. I guess people who learned from puzzling out schematics in the Adafruit/Sparkfun/Shenzhen style with explicit wiring minimized might not understand the value of a good schematic.
The other thing is leaning too far into thinking-by-analogy and trying to make PCB-level design like VLSI design. I don't think it's entirely impossible - if we had better tools for DRC and validation, component-level design would look more like VLSI design. But the design space is so different, with such loose coupling between design, EDA/CAM/simulation, validation, fabricators, assemblers, component vendors, regulators/certifiers, and so on that just getting one corner of it really right would be a major accomplishment.
For any benefit the auto-router adds, it usually ends up costing a project later...
In general, impedance controlled UHF design work with domain specific simulation tools is the modern workflow. Thus, one manually does the critical tracks first, island-pours, and finally power interconnects.
KiCad is slightly better than nothing for layout, and trying to make it into yet another half-baked simulation tool seemed silly. =3
In general, impedance controlled UHF design work with domain specific simulation tools is the modern workflow. Thus, one manually does the critical tracks first, island-pours, and finally power interconnects.
KiCad is slightly better than nothing for layout, and trying to make it into yet another half-baked simulation tool seemed silly. =3
I wish the Kicad team had sunk all the time they put into simulation support (which I rarely use, in or out of kicad) into a proper constraint system instead.
If I need to sim, I’ll just use LTspice or TI TINA - I.e. the simulation ecosystem that includes the part model I need, with no wrangling to get that spice model to play nicely in some other spice toolchain.
If I need to sim, I’ll just use LTspice or TI TINA - I.e. the simulation ecosystem that includes the part model I need, with no wrangling to get that spice model to play nicely in some other spice toolchain.
Micro-cap 12 is still available, was made free, and runs in Wine64 just fine.
https://web.archive.org/web/20230214034946/http://www.spectr...
For learning, the current LTSpice models and QUCS VNA profiled device imported simulation data are reasonable tools as well.
I used KiCad all the time for personal projects, and it has recently already addressed the major issues people found annoying:
1. unstable library version dependency management
2. complex design collaboration over traditional versioning tools like git (still an upstream feature last I checked, but should be available soon.)
3. freezing the specific KiCad and library path structure versions across a project, as assuming every design has a single workstation workflow is a contradictory goal
KiCad is better than most current EDA software, but that is mostly a sad reflection on the traditional tools available. =3
https://web.archive.org/web/20230214034946/http://www.spectr...
For learning, the current LTSpice models and QUCS VNA profiled device imported simulation data are reasonable tools as well.
I used KiCad all the time for personal projects, and it has recently already addressed the major issues people found annoying:
1. unstable library version dependency management
2. complex design collaboration over traditional versioning tools like git (still an upstream feature last I checked, but should be available soon.)
3. freezing the specific KiCad and library path structure versions across a project, as assuming every design has a single workstation workflow is a contradictory goal
KiCad is better than most current EDA software, but that is mostly a sad reflection on the traditional tools available. =3
Where can I learn more about this Kicad git workflow? I’ve heard a lot about it but I’d like to see it for myself.
Did you mean this page?
https://forum.kicad.info/t/tutorial-how-to-enable-git-in-kic...
Or the plugin and content manager under the tools menu for the git-gui module...
YMMV =3
https://forum.kicad.info/t/tutorial-how-to-enable-git-in-kic...
Or the plugin and content manager under the tools menu for the git-gui module...
YMMV =3
I love the ngspice integration in kicad it has gotten really good since kicad 7 or 8.
The tools are fractured, the existing players are lumbering behemoths, and the users are cranky zealots (you will have to pry KiCad out of my cold dead hands).
These last five years have been absolutely incredible to watch in Kicad’s development. Last two releases have added the two major things that Kicad didn’t have that professional CAD tools did:
* database support
* outjob features
Beyond that, it’s really just a matter of adoption and end user preference of how to put these features to work. (Databases tend to come with a lot of internal corporate bureaucracy around organizing data than anything else.)
But, for the topic at hand: don’t you think Kicad is already moving sort of in the direction you talk about, as far as workflow to speed up layout is concerned?
Best example I can think of is the “trace autocomplete” feature in I think 7.0. Hit hotkey (I think it’s F in pcbnew) and the trace will be laid for the track currently being placed. Combine this with the “route from other end of track” (hotkey E), this is a blazing productivity boost, especially when you’re working across two different ball out grids.
Version 9 enabled buses/multiple tracks to be draggable, meaning this whole system could go even faster still.
Many times when starting a design my placements aren't set and that has a huge impact on routing.
Honestly, I’d trust an autorouter to complete a good chunk of my designs if I can get to a placement I’m satisfied with, and I can give the autorouter some constraints on where to route. For example: did a board last year using an NXP iMX8MP with an eMMC. The peripheral ballout on the processor matches the eMMC ballout really nicely - was just a matter of lining the chips up and drawing the lines. An autorouter could have done in seconds what it took me ~10 minutes if it had known to keep everything in the data bus on the top layer.
I think that’s a success criteria autorouter projects suffer from: they don’t consider their autorouter “done” unless their router can do _everything_ on a board. As a working EE, I don’t actually want that. I want an autorouter that can work with me to do a satisfactory job on a little chunk of the design at a time, give me some time to review it for correctness, then move on to the next chunk.
That would be killer. Especially if you could give constraints beyond layers: I.e “keep all nets with names D0-7 on layer one and three, and match their length to 5mm of one another. Use D0 as the net length reference.” If you can do that, then boom: you’ve solved DRAM length tuning. Huge range of design complexity now within reach of the unwashed masses.
OP - if I can find some time to show you what I mean, I’d be more than happy to give you a demo of what I’m talking about.
These last five years have been absolutely incredible to watch in Kicad’s development. Last two releases have added the two major things that Kicad didn’t have that professional CAD tools did:
* database support
* outjob features
Beyond that, it’s really just a matter of adoption and end user preference of how to put these features to work. (Databases tend to come with a lot of internal corporate bureaucracy around organizing data than anything else.)
But, for the topic at hand: don’t you think Kicad is already moving sort of in the direction you talk about, as far as workflow to speed up layout is concerned?
Best example I can think of is the “trace autocomplete” feature in I think 7.0. Hit hotkey (I think it’s F in pcbnew) and the trace will be laid for the track currently being placed. Combine this with the “route from other end of track” (hotkey E), this is a blazing productivity boost, especially when you’re working across two different ball out grids.
Version 9 enabled buses/multiple tracks to be draggable, meaning this whole system could go even faster still.
Many times when starting a design my placements aren't set and that has a huge impact on routing.
Honestly, I’d trust an autorouter to complete a good chunk of my designs if I can get to a placement I’m satisfied with, and I can give the autorouter some constraints on where to route. For example: did a board last year using an NXP iMX8MP with an eMMC. The peripheral ballout on the processor matches the eMMC ballout really nicely - was just a matter of lining the chips up and drawing the lines. An autorouter could have done in seconds what it took me ~10 minutes if it had known to keep everything in the data bus on the top layer.
I think that’s a success criteria autorouter projects suffer from: they don’t consider their autorouter “done” unless their router can do _everything_ on a board. As a working EE, I don’t actually want that. I want an autorouter that can work with me to do a satisfactory job on a little chunk of the design at a time, give me some time to review it for correctness, then move on to the next chunk.
That would be killer. Especially if you could give constraints beyond layers: I.e “keep all nets with names D0-7 on layer one and three, and match their length to 5mm of one another. Use D0 as the net length reference.” If you can do that, then boom: you’ve solved DRAM length tuning. Huge range of design complexity now within reach of the unwashed masses.
OP - if I can find some time to show you what I mean, I’d be more than happy to give you a demo of what I’m talking about.
Hey cushychicken! Would love to see what you’re working on and thanks for explaining (seveibar at tscircuit dot com is my email if you want to set something up!)
I do think humans need to be in the loop on autorouters, but I also think autorouters should be very fast and decent. In web design we progressively add constraints (via CSS) to tweak the output of the visual design and see the result instantly, I believe this same workflow is possible for PCB design (and will favor those good at specifying constraints!)
I do think humans need to be in the loop on autorouters, but I also think autorouters should be very fast and decent. In web design we progressively add constraints (via CSS) to tweak the output of the visual design and see the result instantly, I believe this same workflow is possible for PCB design (and will favor those good at specifying constraints!)
Thanks for the reply. I’ll get in touch via email in the next few days.
I don’t think the goals of “human in the loop” and “fast and decent” are mutually exclusive, or really even at odds with each other.
I just think that most autorouters I’ve tried seem to have been written by CS people who thought PCB layout was an interesting problem to solve - without a particularly deep understanding of what working EEs care about in terms of how you route the board.
Case in point: plenty of autorouters seem to work on the paradigm of “all tracks routed? Great. I’m done.” Never mind that everything is two layers of spaghetti: no ground plane, no power planes, no regard for SI or EMC compliance. In other words: pure hell to test and certify.
Not trying to be crotchety EE by saying this - I can give a good example of what I mean in real time. I also feel like I’m a bit of the exception in the EE community in that I think this is a solvable problem. I just don’t think many CS people have really bothered to consult any real working EEs about their workflows and the downstream portion of test and certification.
I don’t think the goals of “human in the loop” and “fast and decent” are mutually exclusive, or really even at odds with each other.
I just think that most autorouters I’ve tried seem to have been written by CS people who thought PCB layout was an interesting problem to solve - without a particularly deep understanding of what working EEs care about in terms of how you route the board.
Case in point: plenty of autorouters seem to work on the paradigm of “all tracks routed? Great. I’m done.” Never mind that everything is two layers of spaghetti: no ground plane, no power planes, no regard for SI or EMC compliance. In other words: pure hell to test and certify.
Not trying to be crotchety EE by saying this - I can give a good example of what I mean in real time. I also feel like I’m a bit of the exception in the EE community in that I think this is a solvable problem. I just don’t think many CS people have really bothered to consult any real working EEs about their workflows and the downstream portion of test and certification.
I once had to do bringup on an autorouted prototype PCB. The traces between the CPU and DRAM looped three times round the board.
Point 8, the fast dismissal of Monte-Carlo method is a huge blunder.
The point of Monte-Carlo method is that you can tradeoff accuracy for speed. The longer you run your algorithm the more accurate you get.
But what's even more interesting is that we can often use the contrapositive : You can get shitty accurate result real fast.
Instead of exploring all paths, you explore only one path picked at random.
And that's where it shines : when you put it into the most imbricated loop of your algorithm.
For example when you want to learn a neural network which autoroute. Typically you would have the outerloop which is updating the neural network parameter, and the inner-loop which is computing a path through the graph.
When you use Monte-Carlo this inner-loop which control accuracy can be reduced to 1 iteration if everything is bias-less, you will have a slower outerloop due to higher variance but the machine learning should """theoretically""" learn.
This is what allows you to build policies which intuitively always pick the right decision. Like in chess or go, you have some monte-carlo tree search variant like alpha-go-zero or alpha-chess-zero or alpha-router-zero, where even without the search part, the giant cache (encoded by the neural network parameters), once trained, can compute your best guess path in one pass through your neural network aka constant time (with a constant which can easily trade memory for speed by augmenting the number of parameters or training for longer).
The point of Monte-Carlo method is that you can tradeoff accuracy for speed. The longer you run your algorithm the more accurate you get.
But what's even more interesting is that we can often use the contrapositive : You can get shitty accurate result real fast.
Instead of exploring all paths, you explore only one path picked at random.
And that's where it shines : when you put it into the most imbricated loop of your algorithm.
For example when you want to learn a neural network which autoroute. Typically you would have the outerloop which is updating the neural network parameter, and the inner-loop which is computing a path through the graph.
When you use Monte-Carlo this inner-loop which control accuracy can be reduced to 1 iteration if everything is bias-less, you will have a slower outerloop due to higher variance but the machine learning should """theoretically""" learn.
This is what allows you to build policies which intuitively always pick the right decision. Like in chess or go, you have some monte-carlo tree search variant like alpha-go-zero or alpha-chess-zero or alpha-router-zero, where even without the search part, the giant cache (encoded by the neural network parameters), once trained, can compute your best guess path in one pass through your neural network aka constant time (with a constant which can easily trade memory for speed by augmenting the number of parameters or training for longer).
> Point 8, the fast dismissal of Monte-Carlo method is a huge blunder.
Yeah, I had the exact same reaction when I read the article.
MC is the "keeping it real" algorithm, slow, but almost always dead simple to implement and that can be trusted with super high certainty to double-check that you've not completely wandered off in the weeds.
Yeah, I had the exact same reaction when I read the article.
MC is the "keeping it real" algorithm, slow, but almost always dead simple to implement and that can be trusted with super high certainty to double-check that you've not completely wandered off in the weeds.
Which is sort of funny given the intuitive description of Monte Carlo—a random wander, possibly into the weeds.
I was this many days old when I learned the word "imbricated".
Yeah you're right, I should have said nested (it is a valid Anglicism from French "imbriqué(es)" )
Nothing wrong with inadvertently teaching folk new words, it was an uncommon and welcome surprise for me
Yet the author mentioned simulated annealing, so was probably not even trying a neural net, since SA does not compute a gradient.
Simulated annealing has long been one of the classic metaheuristic methods applied to autorouting in circuit design. It was widely used in early research and some commercial tools because its probabilistic approach helps escape local minima in these NP‐hard problems. Today, while many modern autorouters in industry tend to favor faster or more specialized hybrid methods, simulated annealing remains a common benchmark in academic studies and research prototypes for routing and related tasks like floorplanning.
Source: I once wrote a simulated annealer for VLSI wiring. It was cool.
Source: I once wrote a simulated annealer for VLSI wiring. It was cool.
Simulated annealing works great for placing map labels. Lets say you have to label POI with flags, you do not want the flags to overlap and you can only place the labels NSEW of the POI.
1. Plop the labels down in random* directions.
2. Calculate the total overlap.
3. Randomly* move 10% of the labels.
4. Calculate the total overlap again, If the overlap is improved, keep your changes, If things got worse, then backtrack.
5. Sometimes you randomly keep changes that make things worse, this is the annealing part. This is how you escape local minima.
* The randomness does not have to be 100%, you can try smartly avoiding nearest neighbors 2/3 of the time and then do a random move 1/3 of the time. If you always try and be smart you will tend get trapped in a local minima.
1. Plop the labels down in random* directions.
2. Calculate the total overlap.
3. Randomly* move 10% of the labels.
4. Calculate the total overlap again, If the overlap is improved, keep your changes, If things got worse, then backtrack.
5. Sometimes you randomly keep changes that make things worse, this is the annealing part. This is how you escape local minima.
* The randomness does not have to be 100%, you can try smartly avoiding nearest neighbors 2/3 of the time and then do a random move 1/3 of the time. If you always try and be smart you will tend get trapped in a local minima.
That's a great discussion of autorouting. Then he ends with: "key piece to enable the “vibe-building” of electronics." Ouch
Routing itself is easy. It's when the router has to tear up stuff it already routed to fit new stuff in that things get complicated and the combinatorics start to get you.
I miss the autorouter KiCAD used to have. It was taken out for iffy IP reasons (the author had worked for an autorouting company). Reaction to users who wanted it back were along the lines of "Real Men Don't Use Autorouters".[1]
[1] https://forum.kicad.info/t/autorouting-and-autoplacement/185...
Routing itself is easy. It's when the router has to tear up stuff it already routed to fit new stuff in that things get complicated and the combinatorics start to get you.
I miss the autorouter KiCAD used to have. It was taken out for iffy IP reasons (the author had worked for an autorouting company). Reaction to users who wanted it back were along the lines of "Real Men Don't Use Autorouters".[1]
[1] https://forum.kicad.info/t/autorouting-and-autoplacement/185...
Haha I feel like the right reaction to "vibe-*" is to cringe. I cringe a little bit every time I see someone promoting a vibe-coded app at the moment, but I think back to how I got started in coding (I was constantly annoying people on old ActionScript forums to fix my code) and I see so much potential in people being able to get started quickly in any domain. I hope that our autorouter (and others that follow!) will similarly allow people to ship their first electronics without needing tons of guidance or formal education.
That said a good autorouter should also be useful to professionals! So hopefully we help with that as well!
That said a good autorouter should also be useful to professionals! So hopefully we help with that as well!
I wish these folks well and hope that their autorouter gets folded into KiCad.
However, as one of the cranky old people who don't really want to see KiCad expend any energy on autorouters, PCB autorouters are a pain in the ass that never work.
We can look at VLSI autorouters to determine why that is. VLSI autorouters also were a pain in the ass that never worked. But what happened was that VLSI suddenly got lots of layers and you could dedicate a layer to routing vertically, a layer to routing horizontally, a layer to routing power and still have a couple of layers for global vertical interconnect, global horizontal interconnect, and global power.
The fundamental problem with PCB autorouting is that PCBs have WAY more obstructions than VLSI chips do. First, components themselves are obstructions and choke points. Second, PCB vias almost always obstruct all the layers of the board while VLSI vias only obstruct the two layers being connected. Third, PCB vias tend to be bigger than your interconnect metal width. Fourth, the number of PCB layers in use is way smaller than the number of layers in VLSI--the most common numbers of layers are 4 layers (most projects--of which only 2 are really used for general routing), 2 layers (because of cost engineering--good luck autorouting these) and 6 (a tiny minority).
It all adds up to PCB autorouting being a significantly more complicated job than VLSI autorouting.
However, as one of the cranky old people who don't really want to see KiCad expend any energy on autorouters, PCB autorouters are a pain in the ass that never work.
We can look at VLSI autorouters to determine why that is. VLSI autorouters also were a pain in the ass that never worked. But what happened was that VLSI suddenly got lots of layers and you could dedicate a layer to routing vertically, a layer to routing horizontally, a layer to routing power and still have a couple of layers for global vertical interconnect, global horizontal interconnect, and global power.
The fundamental problem with PCB autorouting is that PCBs have WAY more obstructions than VLSI chips do. First, components themselves are obstructions and choke points. Second, PCB vias almost always obstruct all the layers of the board while VLSI vias only obstruct the two layers being connected. Third, PCB vias tend to be bigger than your interconnect metal width. Fourth, the number of PCB layers in use is way smaller than the number of layers in VLSI--the most common numbers of layers are 4 layers (most projects--of which only 2 are really used for general routing), 2 layers (because of cost engineering--good luck autorouting these) and 6 (a tiny minority).
It all adds up to PCB autorouting being a significantly more complicated job than VLSI autorouting.
> 6 (a tiny minority)
I don't think that's true. Perhaps by number of PCBs made 2 and 4 layers dominate: all those IoT doohickeys and alarm clock displays. And even single layer phenolic boards. And for most hobbyist work with little MCUs, 4 layers is a sweet spot. But they're usually either very simple devices where routing is not the problem, or they have very tight application constraints where the placement has to be squeezed and squeezed.
But much of the effort in designing PCBs in industry is on 6+ layers. You can probably smash out a simple smart lightswitch PCB in a day. Boards with BGA SoCs, DDR, PCIe and FPGAs can take whole teams months or more and have incredibly complicated constraints, many of which are implicit (the very simplest: put cap near pin, test points inline, make diff pair via symmetrical, keep this SMPS far from sensitive things, and make the inductor loop as tiny as you can). There are a million ways to make a PCB pass DRC and still be a completely non-functional device. In particular, routing is secondary in terms of effort and importance to placement.
If you sample what a random PCB engineer is working on, it's quite likely to be lots of layers, or an extremely tight layout, and probably both. Or something weird and application-dependent like high voltage. And they're probably fiddling with placement at the random sample time you choose.
Toy examples of sparse layouts like mechanical keyboards and DIP ICs are very unrepresentative of where the real effort, and money, goes.
I don't think that's true. Perhaps by number of PCBs made 2 and 4 layers dominate: all those IoT doohickeys and alarm clock displays. And even single layer phenolic boards. And for most hobbyist work with little MCUs, 4 layers is a sweet spot. But they're usually either very simple devices where routing is not the problem, or they have very tight application constraints where the placement has to be squeezed and squeezed.
But much of the effort in designing PCBs in industry is on 6+ layers. You can probably smash out a simple smart lightswitch PCB in a day. Boards with BGA SoCs, DDR, PCIe and FPGAs can take whole teams months or more and have incredibly complicated constraints, many of which are implicit (the very simplest: put cap near pin, test points inline, make diff pair via symmetrical, keep this SMPS far from sensitive things, and make the inductor loop as tiny as you can). There are a million ways to make a PCB pass DRC and still be a completely non-functional device. In particular, routing is secondary in terms of effort and importance to placement.
If you sample what a random PCB engineer is working on, it's quite likely to be lots of layers, or an extremely tight layout, and probably both. Or something weird and application-dependent like high voltage. And they're probably fiddling with placement at the random sample time you choose.
Toy examples of sparse layouts like mechanical keyboards and DIP ICs are very unrepresentative of where the real effort, and money, goes.
KiCAD must be moving up in the world if people are using it for 6-layer boards. Or Altium, at US$5,500/year, is now too expensive even for pros.
I'd thought of KiCAD as a hobbyist tool. It didn't have the intensive library support of a pro tool. Footprints and 3D models were user submissions and not well curated or tested. Footprints might need some manual tweaking. With a pro tool, you're paying for a small army of people doing data entry on part info. Has KiCAD improved in that area?
I'd thought of KiCAD as a hobbyist tool. It didn't have the intensive library support of a pro tool. Footprints and 3D models were user submissions and not well curated or tested. Footprints might need some manual tweaking. With a pro tool, you're paying for a small army of people doing data entry on part info. Has KiCAD improved in that area?
> KiCAD must be moving up in the world if people are using it for 6-layer boards.
With every release of KiCad, the difference between KiCad and Altium gets smaller and smaller.
> With a pro tool, you're paying for a small army of people doing data entry on part info.
That has never been my experience using any PCB tool (even the truly big boys like Expedition or Allegro). Any libraries managed by an entity external to the people designing PCB boards have always been a disaster. If you haven't personally put something on a board, it is, practically by definition, broken.
Anyone using a "pro" tool (Expedition or Allegro) has their own army of people managing their library.
I could rant yet again about how Altium screwed themselves by encrypting libraries so that you had to subscribe to them, but I'm tired of that rant.
In Altium's stead, KiCad has been eating up the bottom end more and more. There are still some missing features (copper on inner footprint layers, flex board stackups), but they get fewer and fewer with each release. And Autodesk's moves with Eagle have hastened even more people onto KiCad.
With every release of KiCad, the difference between KiCad and Altium gets smaller and smaller.
> With a pro tool, you're paying for a small army of people doing data entry on part info.
That has never been my experience using any PCB tool (even the truly big boys like Expedition or Allegro). Any libraries managed by an entity external to the people designing PCB boards have always been a disaster. If you haven't personally put something on a board, it is, practically by definition, broken.
Anyone using a "pro" tool (Expedition or Allegro) has their own army of people managing their library.
I could rant yet again about how Altium screwed themselves by encrypting libraries so that you had to subscribe to them, but I'm tired of that rant.
In Altium's stead, KiCad has been eating up the bottom end more and more. There are still some missing features (copper on inner footprint layers, flex board stackups), but they get fewer and fewer with each release. And Autodesk's moves with Eagle have hastened even more people onto KiCad.
People 100% use KiCad for 6+ layers. My most recent design has 6 and KiCad didn't break a sweat. There's a 12-layer CERN design that ships with KiCad as a demo, even.
No tool has libraries that never need tweaking for a design. 99% of KiCad library parts will never need touching in a usual "simple" design, but no one library part can cover all eventualities. Any tool that promises a library that doesn't ever need to be touched is lying or naive. You should also always check footprints wherever they come from, even if you paid for them.
KiCad has a new library team, and the parts in the library are revolutionarily better then they were say, 5 years ago.
No tool has libraries that never need tweaking for a design. 99% of KiCad library parts will never need touching in a usual "simple" design, but no one library part can cover all eventualities. Any tool that promises a library that doesn't ever need to be touched is lying or naive. You should also always check footprints wherever they come from, even if you paid for them.
KiCad has a new library team, and the parts in the library are revolutionarily better then they were say, 5 years ago.
Author here. Lots of great points being made. I want to throw in a crazy prediction.
I think routing is basically an image transformer problem without a good dataset right now. If the eye of the giant AI companies turned to autorouting and large synthetic but physically simulated circuit datasets were created, we would basically be done and autorouting would be a solved problem.
This means that all the work I’m doing now on heuristic algorithms, as well as all the hard work done by humans, will probably not be needed in the future. I just don’t see autorouting as being more difficult (in terms of solution space size) than the art being produced by transformer models right now.
I’m saying this because you’re right, these heuristic algorithms can only get us so far- the problem is really difficult. But our human intuition, the magic black box operation we do, doesn’t seem to be too far beyond the emerging transformer image models.
I think routing is basically an image transformer problem without a good dataset right now. If the eye of the giant AI companies turned to autorouting and large synthetic but physically simulated circuit datasets were created, we would basically be done and autorouting would be a solved problem.
This means that all the work I’m doing now on heuristic algorithms, as well as all the hard work done by humans, will probably not be needed in the future. I just don’t see autorouting as being more difficult (in terms of solution space size) than the art being produced by transformer models right now.
I’m saying this because you’re right, these heuristic algorithms can only get us so far- the problem is really difficult. But our human intuition, the magic black box operation we do, doesn’t seem to be too far beyond the emerging transformer image models.
The major difference is in PCB, every single track has to abide by the rules, no exceptions are allowed if you want your board to work.
While AI-generated art is full of small defects which people are just ignoring, who cares about non-natural shadows or unrealistically large fingers.
It is possible to iteratively combine AI with DRC checker and loop until all is good, but it's not obvious to me that this would be performant enough, or if this system will stay in some sort of local minimum forever once the circuit is complex enough.
While AI-generated art is full of small defects which people are just ignoring, who cares about non-natural shadows or unrealistically large fingers.
It is possible to iteratively combine AI with DRC checker and loop until all is good, but it's not obvious to me that this would be performant enough, or if this system will stay in some sort of local minimum forever once the circuit is complex enough.
The same way claude never outputs code that has a syntax error, the image transformers will output DRC compliant “images”!
I think spatial partitioning can help solve issues with minor DRC violations as well- it should be easier to correct an image than to generate one from scratch. But I’m not even sure it’ll be necessary because of how coherent image models already are.
I think spatial partitioning can help solve issues with minor DRC violations as well- it should be easier to correct an image than to generate one from scratch. But I’m not even sure it’ll be necessary because of how coherent image models already are.
Claude doesn't usually produce code that actually works though. Passing DRC is one thing (no syntax errors). Actually works is another (compiles and executes with the desired effect as a complete application).
And you don't even get to use unit tests to check correctness.
And you don't even get to use unit tests to check correctness.
You're suggesting the robots can learn the routing algorithms and rules just by looking at a bunch of pictures?
Sure, maybe, given a super-massive amount of data.
I see it as the difference between "I want a photo-realistic portrait of a beagle" and "I want a photo-realistic portrait of my neighbor's beagle, Bob". The first one there's some general rules as to what makes something a 'beagle' so is not too hard while the second has specific constraints which can't be solved without a bunch of pictures of Bob.
To address the specific issue, an AI would have to learn the laws of physics (aka, "Bobness") from a bunch of pictures of, essentially, beagles in order to undertake the task at hand.
Sure, maybe, given a super-massive amount of data.
I see it as the difference between "I want a photo-realistic portrait of a beagle" and "I want a photo-realistic portrait of my neighbor's beagle, Bob". The first one there's some general rules as to what makes something a 'beagle' so is not too hard while the second has specific constraints which can't be solved without a bunch of pictures of Bob.
To address the specific issue, an AI would have to learn the laws of physics (aka, "Bobness") from a bunch of pictures of, essentially, beagles in order to undertake the task at hand.
> would have to learn the laws of physics
I forgot the name unfortunately but there was a project recently where they made AI + physically correct modeling.
EDIT found it, the Genesis project: https://x.com/zhou_xian_/status/1869511650782658846
I forgot the name unfortunately but there was a project recently where they made AI + physically correct modeling.
EDIT found it, the Genesis project: https://x.com/zhou_xian_/status/1869511650782658846
In agreement.
I think maybe the best way to get this data set is to subsidize a few dozen electronics recycling centers for every unique microCT scan they send you. Lease them the tomography equipment. They increase their bottom line, you get a huge dataset of good-to-excellent quality commercial PCB designs.
I think maybe the best way to get this data set is to subsidize a few dozen electronics recycling centers for every unique microCT scan they send you. Lease them the tomography equipment. They increase their bottom line, you get a huge dataset of good-to-excellent quality commercial PCB designs.
Very fun idea, I had not considered training on existing work (IP is so sensitive I just couldn't think of a way to get enough)
My approach is slightly different for building the dataset. I think we should bootstrap an absolutely massive synthetic dataset full of heuristically autorouted PCBs to allow the AI to learn the visual token system and basic DRC compliance. We then use RL to reward improvements on the existing designs. Over time the datasets will get better similar to how synthetic datasets are produced whenever a new LLM model is released that make training subsequent LLMs easier.
I think people are underestimating the number of PCBs that are needed to train a system like this. My guess is it is well over 10m PCBs with perfect fidelity. It will make sense to have a large synthetic data strategy.
My approach is slightly different for building the dataset. I think we should bootstrap an absolutely massive synthetic dataset full of heuristically autorouted PCBs to allow the AI to learn the visual token system and basic DRC compliance. We then use RL to reward improvements on the existing designs. Over time the datasets will get better similar to how synthetic datasets are produced whenever a new LLM model is released that make training subsequent LLMs easier.
I think people are underestimating the number of PCBs that are needed to train a system like this. My guess is it is well over 10m PCBs with perfect fidelity. It will make sense to have a large synthetic data strategy.
Before you splurge on hardware to extract data it would be much cheaper and faster to just buy it in Shenzhen. All the Apple stuff has been reverse engineered, this is how apps like ZXW have scans of all pcb layers. Random google search https://www.diyfixtool.com/blogs/news/iphone-circuit-diagram...
Article makes some important points - especially re visualisation and cache effects.
Some quibbles or glossovers:
> A recursive algorithm is a depth first search. Any loop that explores candidates/neighbors without sorting the candidates is a BFS.
Not sure what to say about this. It's either wrong or I'm missing the intuition. Both DFS and BFS can either be written iteratively or recursively; the real difference is whether you pop your next candidate from the top or the bottom of the stack. Equivalently, whether you use a stack (FILO) or a queue (FIFO). That said, I took maths rather than CS so verify this for yourself.
> It is simply the best foundation for any kind of informed search (not just for 2d grids!)
A* is useful in pathfinding if you have some notion of easily-computed "distance" to your desired target and you're only running a few queries for any given graph. If you plan to run many queries on a mostly-static graph (like a road network) then you could be better off applying a pre-processing algorithm like contraction heirarchies. If you're optimising but don't know what target you're aiming for (e.g. Traveling Salesman) then using some other local search heuristic like 2-opt may be better.
> The major difference between these [A* and BFS] is BFS explores all adjacent nodes, while A* prioritizes exploring nodes that are closer to the destination.
This is definitely a difference. But it glosses over the biggest difference, which is that A* is a dynamic algorithm. That allows you to terminate early with confidence that you have found the shortest path. With BFS you may not be certain until you've searched the whole graph, which could be vast.
Some quibbles or glossovers:
> A recursive algorithm is a depth first search. Any loop that explores candidates/neighbors without sorting the candidates is a BFS.
Not sure what to say about this. It's either wrong or I'm missing the intuition. Both DFS and BFS can either be written iteratively or recursively; the real difference is whether you pop your next candidate from the top or the bottom of the stack. Equivalently, whether you use a stack (FILO) or a queue (FIFO). That said, I took maths rather than CS so verify this for yourself.
> It is simply the best foundation for any kind of informed search (not just for 2d grids!)
A* is useful in pathfinding if you have some notion of easily-computed "distance" to your desired target and you're only running a few queries for any given graph. If you plan to run many queries on a mostly-static graph (like a road network) then you could be better off applying a pre-processing algorithm like contraction heirarchies. If you're optimising but don't know what target you're aiming for (e.g. Traveling Salesman) then using some other local search heuristic like 2-opt may be better.
> The major difference between these [A* and BFS] is BFS explores all adjacent nodes, while A* prioritizes exploring nodes that are closer to the destination.
This is definitely a difference. But it glosses over the biggest difference, which is that A* is a dynamic algorithm. That allows you to terminate early with confidence that you have found the shortest path. With BFS you may not be certain until you've searched the whole graph, which could be vast.
> recursive == DFS
The intuition there is that people tend to write algorithms recursively when they easily map to interactions with the top of the stack, since that's easier to express in most languages than bringing in an external stack to think about. Hence, if you see something recursive in the wild it likely looks more like DFS than BFS. As you point out, that isn't a hard rule.
The intuition there is that people tend to write algorithms recursively when they easily map to interactions with the top of the stack, since that's easier to express in most languages than bringing in an external stack to think about. Hence, if you see something recursive in the wild it likely looks more like DFS than BFS. As you point out, that isn't a hard rule.
Indeed, BFS, DFS, and A* are the same algorithm just with a different data structure to track unexplored nodes. BFS uses a FIFO queue, DFS uses a LIFO stack, and A* uses a priority queue (generally a heap)
A* is also importantly different for what the ordering of the priority queue is. Dijkstra's algorithm is just BFS with a priority queue, but the lack of heuristic in the comparison key keeps it clearly breadth-first.
Yeah. And A* simply generalises Dijkstra's algorithm by adding a distance heuristic to the priority expression.
No you don't have to search whole graph with BFS (it might happen if source and target is on opposite corners), first time you reach a node you know 100% that it is the shortest path. That's one of basic invariants allowing BFS to produce correct result, you can terminate early when all targets are reached. A difference between A* and BFS is that instead of searching shortest path between two points BFS finds shortest path from single point to ALL points in graph. A* makes a tradeof of speeding up individual query by answering to weaker question. When problem allows it replacing thousands of A* calls by single BFS or Dijkstra call can give a major speedup. Another important difference is that BFS only works on graphs where all edges have same length, A* supports mixed edge lengths. They are not interchangeable, just like finding minimum element in a list isn't replacement for sorting the list.
This is wrong or at least misleading in a couple of ways:
A* is not limited to point-to-point, it also handles point-to-set-of-points just fine (you just might have to reverse the direction you're thinking in). Although this is most often used for "we only need a path to one of them", it's still faster even if you need a path to all of them due to merging. Visiting all points (as BFS does) on the graph is rarely what you actually want (though admittedly, if you're able to throw away the grid it becomes more likely; like the article, I do find people use grids far too often).
BFS works just fine with variable edge weights, you just have to use a priority queue instead of a plain queue for the next set of nodes to be visited.
A* is not limited to point-to-point, it also handles point-to-set-of-points just fine (you just might have to reverse the direction you're thinking in). Although this is most often used for "we only need a path to one of them", it's still faster even if you need a path to all of them due to merging. Visiting all points (as BFS does) on the graph is rarely what you actually want (though admittedly, if you're able to throw away the grid it becomes more likely; like the article, I do find people use grids far too often).
BFS works just fine with variable edge weights, you just have to use a priority queue instead of a plain queue for the next set of nodes to be visited.
BFS with priority queue isn't BFS it's called Dijkstra's algorithm.
Are you sure it works correctly for shortest path to all points in set not just "we need one of them" case? When running in reverse I would expect closest point to be priotized first, which would potentially mark all the area around target as visited thus blocking paths from other points in set from reaching it. It's equivalent to introducing one more point in graph and connecting it to all the points in set. Unless it works for one to all in set it's a weaker result than what Dijkstra or BFS answers. If your problem doesn't need it, don't use it. But that's my point use the weakest algorithm which directly answers required query, and be aware that building more powerful algorithm by repeatedly calling more specialized but weaker one won't always be optimal.
Are you sure it works correctly for shortest path to all points in set not just "we need one of them" case? When running in reverse I would expect closest point to be priotized first, which would potentially mark all the area around target as visited thus blocking paths from other points in set from reaching it. It's equivalent to introducing one more point in graph and connecting it to all the points in set. Unless it works for one to all in set it's a weaker result than what Dijkstra or BFS answers. If your problem doesn't need it, don't use it. But that's my point use the weakest algorithm which directly answers required query, and be aware that building more powerful algorithm by repeatedly calling more specialized but weaker one won't always be optimal.
A* can be tweaked to solve one-to-many shortest paths. You need to be careful about how you aggregate the heuristic function over all the targets [0]. Also, the performance improvements vs Dijkstra would practically disappear in many scenarios, for example if your source is "in the middle" of all the targets. In that case there's no benefit to biasing your search towards any particular target: you might as well simply expand outwards as you do in ordinary Dijkstra.
For road network routing (which is more my thing) you are orders of magnitude better off pre-processing the graph with contraction heirarchies [1] and then using a specialised algorithm such as RPhast [2] at query time.
[0] https://www.semanticscholar.org/paper/Heuristic-search-for-o...
[1] https://en.wikipedia.org/wiki/Contraction_hierarchies
[2] https://www.microsoft.com/en-us/research/publication/faster-...
For road network routing (which is more my thing) you are orders of magnitude better off pre-processing the graph with contraction heirarchies [1] and then using a specialised algorithm such as RPhast [2] at query time.
[0] https://www.semanticscholar.org/paper/Heuristic-search-for-o...
[1] https://en.wikipedia.org/wiki/Contraction_hierarchies
[2] https://www.microsoft.com/en-us/research/publication/faster-...
> BFS works just fine with variable edge weights, you just have to use a priority queue instead of a plain queue for the next set of nodes to be visited.
But usually you don't call it BFS if it's using a priority queue?
But usually you don't call it BFS if it's using a priority queue?
> first time you reach a node you know 100% that it is the shortest path
As you note later in your comment, this is only true in the case of uniform edge weights. In general you can come up with easy counterexamples e.g. a very long direct edge from source to target vs lots of short edges which sum to a smaller amount.
As you note later in your comment, this is only true in the case of uniform edge weights. In general you can come up with easy counterexamples e.g. a very long direct edge from source to target vs lots of short edges which sum to a smaller amount.
If you have mixed edges you don't use BFS you use Dijkstra's algorithm preferably with a heap and the example you said is still not a problem. The short path made of many edges would be pulled out of heap first ensuring first time visit is still the shortest. If you run actual BFS on non uniform edges, for something like a->b a->c b->d c->d d->e it won't ecesseryn even produce the shortest path at all, only way i can imagine it working is to drop the requirement of not visiting single node twice for it to produce shortest path. At that point it's not BFS at all, and not only you would have to visit whole graph you would have to visit whole graph multiple times, worst case expontially if you have something like braching chain of paths.
We are in agreement. None of this contradicts my original points. You can use BFS on a general weighted graph but you would have to search the entire graph to be sure of the shortest path. That's why no one does it.
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> The QuadTree and every general-purpose tree data structure are insanely slow. Trees are not an informed representation of your data.
> Any time you’re using a tree you’re ignoring an O(~1) hash algorithm for a more complicated O(log(N)) algorithm
This is incredibly misguided. The hashing approach is fine if your points are evenly distributed and you only really want to query an area relatively close to the fixed subdivisions you've chosen, otherwise your 0(1) is going to degenerate to O(n).
Trees are an informed representation when you don't know how your data is distributed.
Similar story for randomized algorithms. What happens when your search space is many trillions of items or possibilities? Or there are no heuristics to be had? If you can't brute force it and you can't use a clever algorithm then randomized algorithms are a savior.
Maybe these aren't needed for this specific application, but best to not make sweeping statements.
> Any time you’re using a tree you’re ignoring an O(~1) hash algorithm for a more complicated O(log(N)) algorithm
This is incredibly misguided. The hashing approach is fine if your points are evenly distributed and you only really want to query an area relatively close to the fixed subdivisions you've chosen, otherwise your 0(1) is going to degenerate to O(n).
Trees are an informed representation when you don't know how your data is distributed.
Similar story for randomized algorithms. What happens when your search space is many trillions of items or possibilities? Or there are no heuristics to be had? If you can't brute force it and you can't use a clever algorithm then randomized algorithms are a savior.
Maybe these aren't needed for this specific application, but best to not make sweeping statements.
Measure measure measure measure. Each case is different.
But more seriously I think tree based algos tend to be overhyped, and people get a bit absorbed into thinking about big-O behavior when the constant factors are super important even when you’re looking at hundreds of thousands of elements. Not to mention stuff like data locality. Like sometimes your computer will be faster at just looking in a seq scan rather than dealing with the bookkeeping of a fancier structure. Sometimes.
I think a nicer argument overall is to build a tiny wrapper around your operations, build out what is easy, and then figure it out through measurements.
Worst case you end up entirely rewriting your program to accommodate a different structure to try for better perf but in my experience rewriting entire files from scratch tends to get you a good number of improvements for free.
But more seriously I think tree based algos tend to be overhyped, and people get a bit absorbed into thinking about big-O behavior when the constant factors are super important even when you’re looking at hundreds of thousands of elements. Not to mention stuff like data locality. Like sometimes your computer will be faster at just looking in a seq scan rather than dealing with the bookkeeping of a fancier structure. Sometimes.
I think a nicer argument overall is to build a tiny wrapper around your operations, build out what is easy, and then figure it out through measurements.
Worst case you end up entirely rewriting your program to accommodate a different structure to try for better perf but in my experience rewriting entire files from scratch tends to get you a good number of improvements for free.
Sequential scans are terribly underrated given today's architectures. If the datum is small and it's like 1000 elements max it's going to be hard to beat a simple scan of an array for speed and memory efficiency.
But trees et al also make a lot of sense as a default. If you need the performance to scale in a roughly linear manner you need to be smart about it. This is what sinks a lot of software, usability wise.
But trees et al also make a lot of sense as a default. If you need the performance to scale in a roughly linear manner you need to be smart about it. This is what sinks a lot of software, usability wise.
Author here. This was almost worth a mention in the article. Every once in a while I'd have a friend take a look at a piece of my algorithm and they'd suggest swapping out a sort() function for a priority queue etc. (without looking at the performance data) and find no change or sometimes even a slightly worse result! This is why having performance data is so critical, the performance of a complex algorithm can be counter-intuitive and most performance "gotchas" are just straight up mistakes or recalculations rather than fundamental algorithm issues (remember the GTA5 bug that caused the loading screen to take an extra 5 seconds, and was simple enough for a random dev to patch? that is the most common type of performance issue in development!)
For 3D I found octtrees very effective and fast. In my implementation you can even move items around without having to regenerate the tree.
I have yet to find a satisfactory way to store points (in 2d or 3d) and be able to query nearby points. a kD tree is nice, but I want to add points as I go, not build a structure around a fixed set.
I have yet to find a satisfactory way to store points (in 2d or 3d) and be able to query nearby points. a kD tree is nice, but I want to add points as I go, not build a structure around a fixed set.
I suspect that thinking in trie terms is the way to go, where each node chooses its preferred representation.
The top level should usually use a grid, though uniform-density children can also use one. The grid scale should be chosen to maximize low-density children while minimizing empty children.
For low-density children, just store an unsorted bunch of items, or maybe sort them on just one axis (preferably the outermost axis used for rendering). If your outer level is actually uniform, all of its children will be this (or empty).
For high-density children, use a quadtree/octree ... or just treat them as opaque objects to be handled recursively. These still suck for all the memory-walking they do, but since you've handled the outer part using the grid, the overhead is smaller. These can do "nearby" queries if your implementation is good, but many implementations suck. Making them mutable just means you need to either implement rebalancing or just stick to fixed planes.
The top level should usually use a grid, though uniform-density children can also use one. The grid scale should be chosen to maximize low-density children while minimizing empty children.
For low-density children, just store an unsorted bunch of items, or maybe sort them on just one axis (preferably the outermost axis used for rendering). If your outer level is actually uniform, all of its children will be this (or empty).
For high-density children, use a quadtree/octree ... or just treat them as opaque objects to be handled recursively. These still suck for all the memory-walking they do, but since you've handled the outer part using the grid, the overhead is smaller. These can do "nearby" queries if your implementation is good, but many implementations suck. Making them mutable just means you need to either implement rebalancing or just stick to fixed planes.
Almost everything matches my gamedev heuristics. I even have empathy for choosing JS. I'm making a game modding framework right now that operates on a lispy kind of s-expressions. Optimizing to accelerate creative iteration time > *, I've found.
A*, Lee's algorithm and the like are all cool. It's criminal to write any kind of floodfill without having an accompanying visualization, you're squandering so much dopamine.
This article has me wondering if all the gamedev things I *didn't* read about but are adjacent have utility in this kind of thing. I can't be the first person to think a boids router would be pretty fun. More seriously, I bet jumpflooding signed distance fields would provide you a lot of power.
Everything about spatial hashing in particular matches my experience. Haven't found many occurences in almost 2 decades where any of the tree structures are worth the time. One notable exception. The lovecraftian text editor I made uses quite a lot of trie's for reactivity things. Nice way to compress 45,000 words into a compact state machine for event handling.
A*, Lee's algorithm and the like are all cool. It's criminal to write any kind of floodfill without having an accompanying visualization, you're squandering so much dopamine.
This article has me wondering if all the gamedev things I *didn't* read about but are adjacent have utility in this kind of thing. I can't be the first person to think a boids router would be pretty fun. More seriously, I bet jumpflooding signed distance fields would provide you a lot of power.
Everything about spatial hashing in particular matches my experience. Haven't found many occurences in almost 2 decades where any of the tree structures are worth the time. One notable exception. The lovecraftian text editor I made uses quite a lot of trie's for reactivity things. Nice way to compress 45,000 words into a compact state machine for event handling.
It is a really fun idea to build a boids router (shelving that for a future article!) I wrote previously about recursive pattern autorouters which are really good at having small solution spaces (and therefore, easier to get conventional machine learning algorithms to predict). There are so many interesting unexplored areas in autorouting!!
I hadn't heard of jumpflooding (for others: fast, parallel algorithm for approximating distance fields), that could definitely be interesting, thanks for the tip!
I hadn't heard of jumpflooding (for others: fast, parallel algorithm for approximating distance fields), that could definitely be interesting, thanks for the tip!
I think the trees were a lot more useful in the past when memory and caches were smaller (and I suspect they can still be useful for precomputation, though I'd have to sit down and benchmark fixed-grid-with-smart-sizing vs. tree). Trees are also amendable to recursive algorithms but the author has noted that they have reasons to choose iterative over recursive algorithms, so these pieces of advice synergize.
(It is perhaps worth noting: broadly speaking, "recursive" vs. "non-recursive" is a bit of an artificial distinction. The real question is "does a pre-baked algorithm with rigid rules handle flow control, or do you?" If you care about performance a lot, you want the answer to be you, so having your run state abstracted away into an execution-environment-provided stack that you can't easily mutate weirdly at runtime begins to get in your way).
(It is perhaps worth noting: broadly speaking, "recursive" vs. "non-recursive" is a bit of an artificial distinction. The real question is "does a pre-baked algorithm with rigid rules handle flow control, or do you?" If you care about performance a lot, you want the answer to be you, so having your run state abstracted away into an execution-environment-provided stack that you can't easily mutate weirdly at runtime begins to get in your way).
> 95% of your focus should be on reducing the number of iterations. This is why language doesn’t matter.
And then once you've used the playful, expressive (interpreted, abstract, slow) language you enjoy using, to develop an excellent, performant algorithm... if performance still matters, write the same thing again in a performant, low-level language, and perhaps even write architecture-specific assembly.
There's a reason numpy, pandas, OpenCV, TensorFlow aren't written in pure Python, but instead let Python tell them to do things they've implemented in high performance C++/assembly/CUDA, etc.
No matter how proud the authors would be of exploring a problem space and coming up with an efficient algorithm (and blogging about it), I doubt they'd be popular numerical computing libraries if they insisted on it being written in pure Python, or Javascript.
While this is a fun blog post, I don't think it'd have the same takeaways if the author's algorithmic insights got a pure-Javascript HEVC encoder down from 1 day per frame to 3 hours per frame...
And then once you've used the playful, expressive (interpreted, abstract, slow) language you enjoy using, to develop an excellent, performant algorithm... if performance still matters, write the same thing again in a performant, low-level language, and perhaps even write architecture-specific assembly.
There's a reason numpy, pandas, OpenCV, TensorFlow aren't written in pure Python, but instead let Python tell them to do things they've implemented in high performance C++/assembly/CUDA, etc.
No matter how proud the authors would be of exploring a problem space and coming up with an efficient algorithm (and blogging about it), I doubt they'd be popular numerical computing libraries if they insisted on it being written in pure Python, or Javascript.
While this is a fun blog post, I don't think it'd have the same takeaways if the author's algorithmic insights got a pure-Javascript HEVC encoder down from 1 day per frame to 3 hours per frame...
Man, look at all those keywords I remember from college. I wish I got to use fancy well-known algorithms. Instead all I'm doing is building UI components and REST APIs to present elasticsearch results. All the fun stuff is buried in black boxes.
Algorithms are a lot more fun now that LLMs have all the geometric heuristics memorized. I think a lot of algorithms are just inescapable in game development, so if you're itching to make an algorithm making something like like a tower defense game has a lot of classic algorithms involved!
Llms do not have it memorized. Try asking it to calculate the signed angle between two signed planes.
I gave up and told it to copy the unity implementation.
Right. There was a time when I had a well-worn copy of Knuth, vol. 1 at hand. No longer.
The core problem is a severe mismatch between academic curricula and what the job market actually needs on one side and the fact that companies use "needs a college degree" as a proxy to weed out risk and bypass ADA/anti discrimination laws. Both are a massive drain on the economy.
IMHO, at the very least the current CS degree needs to be split up - CS should be split into various smaller (and faster achievable) subdegrees - the fancy math stuff should be its own degree, possibly be fused with a new degree relating to AI, database and network theory should be its own degree, and frankly stuff like low-level assembler as well, and the "how do electronic components, NAND gates, boolean logic and whatnot work" moved off to electronics engineering. And what the market actually needs the most - people who can crank out CRUD app stuff - should be either its own degree if one insists that this needs academic knowledge, or be moved off to something like trades education.
In parallel, the job requirements gatekeeping should be tackled by laws - companies must no longer be allowed to require degrees that have little to no impact on the actual job duties. It's forcing our children to waste years of their life and take on five to six figures worth of debt, all only for companies to be able to weed out people.
IMHO, at the very least the current CS degree needs to be split up - CS should be split into various smaller (and faster achievable) subdegrees - the fancy math stuff should be its own degree, possibly be fused with a new degree relating to AI, database and network theory should be its own degree, and frankly stuff like low-level assembler as well, and the "how do electronic components, NAND gates, boolean logic and whatnot work" moved off to electronics engineering. And what the market actually needs the most - people who can crank out CRUD app stuff - should be either its own degree if one insists that this needs academic knowledge, or be moved off to something like trades education.
In parallel, the job requirements gatekeeping should be tackled by laws - companies must no longer be allowed to require degrees that have little to no impact on the actual job duties. It's forcing our children to waste years of their life and take on five to six figures worth of debt, all only for companies to be able to weed out people.
This is great. As someone not working directly on 2D / 3D space problems, my biggest take-away is the value of visualizing things. Humans are really good at grasping and analyzing pictures. Another is the idea to use a stochastic or brute-force method to understand the shape of the problem, so to say, and choose a better method based on that, not from a theoretic understanding alone.
> 2. Implementation Language doesn’t matter
> I’m controversially writing our autorouter in Javascript. This is the first thing people call out, but it’s not as unreasonable as you might expect. Consider that when optimizing an algorithm, you’re basically looking at improving two things:
> Lowering the number of iterations required (make the algorithm smart) Increasing the speed of each iteration
It may be true in this domain, I wouldn’t know, but applied to software engineering in general IMO it would be a massively incorrect assumption to say choice of language doesn’t affect speed and needed number of iterations.
> I’m controversially writing our autorouter in Javascript. This is the first thing people call out, but it’s not as unreasonable as you might expect. Consider that when optimizing an algorithm, you’re basically looking at improving two things:
> Lowering the number of iterations required (make the algorithm smart) Increasing the speed of each iteration
It may be true in this domain, I wouldn’t know, but applied to software engineering in general IMO it would be a massively incorrect assumption to say choice of language doesn’t affect speed and needed number of iterations.
I think there's a fair argument to be made that when you're chasing Big O algorithmic improvements, then the effective constant terms incurred by "faster or slower language execution" are premature optimisation. The difference between Rust or hardcoded assembler compared to Javascript or VisualBasic are pretty irrelevant if you're still trying to get your exponent or polynomial terms under control.
The argument falls apart when the premise that you're chasing big O does.
Poor cache/memory/disk accesses can result in constant regressions so vast that a 'worse' algorithm actually fares better. Also, we tend to falsely settle on referring to 'O' rather than omega, theta, and average, even when we don't care about worse-case or contrived workloads. See quicksort and mergesort.
For a similar concept in another domain, see also the external memory model:
https://en.algorithmica.org/hpc/external-memory/model/
Poor cache/memory/disk accesses can result in constant regressions so vast that a 'worse' algorithm actually fares better. Also, we tend to falsely settle on referring to 'O' rather than omega, theta, and average, even when we don't care about worse-case or contrived workloads. See quicksort and mergesort.
For a similar concept in another domain, see also the external memory model:
https://en.algorithmica.org/hpc/external-memory/model/
Yep.
Also, js is easy to iterate on - it's more lenient than rust or say c#. Especially if you are exploring thinks, that's a huge boon.
Obviously, for the same algorithm, compiled languages will be slower. Does it matter? Maybe. But time to find this algorithm is also critical - and if js helps here, it's a good choice.
But what about afterwards? You move to a more performant language or just accept that you're now invested in JS?
One or the other, probably — Facebook were sufficiently invested in PHP that they released a whole new VM for the language. In Python, one would relatively commonly extract the performance-critical sections into C (or, nowadays, Rust) and you could do the same with Node too. Or the JIT might be fast enough that it's not worthwhile.
I think Javascript might doom the autorouter to small scale designs or very long processing times, but I have never used tscircuit so maybe I'm wrong.
Believe in the cache! There's a reason most websites can be written in javascript now and not have major issues, the I/O bottleneck is more important than the raw computation. As long as the algorithm is cacheable (and this means each stage maintains partial/spatial caches!) the implementation language shouldn't be very important.
There are specific NP Hard problems that aren't spatially cacheable and we may need WASM for, but these problems tend to be nearly impossible for humans as well, so PCB designers will opt to just add some space on the board or add additional layers.
There are specific NP Hard problems that aren't spatially cacheable and we may need WASM for, but these problems tend to be nearly impossible for humans as well, so PCB designers will opt to just add some space on the board or add additional layers.
I want to love hardware as code. I really do. I love infrastructure as code, devops as codezyou nake it. But I just don't think it's ever going to happen. At least not fully. Something about it to me just has to be graphical. Maybe it's the information density, or the fact that circuits are graphs / networks, or maybe it's just habit.
On the topic of autorouters, the post doesn't really go into why there are no open source data sets and why there aren't many open source tools. It's probably no surprise but there's a TON of money in it. The author mentions iphones and 10s of thousands of routes. That's nothing. Modern ICs have billions of transistors and billions of routes with an insanely complicated rule set governing pitch, spacing, jobs, etc. Not to mention the entire time you have to make sure the parasitic resistance and capacitance from the routing doesn't break the timing assumptions.
Cadence and Synopsys probably put billions yearly into R&D and I bet a big chunk goes into "Place & Route". I've heard that licenses for their tools run into the miions of dollars per head per year.
On the topic of autorouters, the post doesn't really go into why there are no open source data sets and why there aren't many open source tools. It's probably no surprise but there's a TON of money in it. The author mentions iphones and 10s of thousands of routes. That's nothing. Modern ICs have billions of transistors and billions of routes with an insanely complicated rule set governing pitch, spacing, jobs, etc. Not to mention the entire time you have to make sure the parasitic resistance and capacitance from the routing doesn't break the timing assumptions.
Cadence and Synopsys probably put billions yearly into R&D and I bet a big chunk goes into "Place & Route". I've heard that licenses for their tools run into the miions of dollars per head per year.
I had a play with the tscircuit CAD tool which they're writing this autorouter for. It's hardware-as-code, leveraging React.
I love the concept of schematics-as-code. But layout-as-code is awful. Maybe AI tools will improve it enough - but unless there's a user friendly escape hatch which lets you tweak things with a mouse, it's a dead end.
I've had the same problem with auto-generated wiring diagrams and flowcharts. Great in theory, but then the tool generates some ugly thing you can't make sense of and there's no way to change it.
There's definitely tremendous potential for AI in electronic design. But I think the really complicated bit is component selection and library management - you have to somehow keep track of component availability, size and a range of parameters which are hidden on page 7 of a PDF datasheet. Not to mention separate PDF application notes and land patterns.
Compared to all that, connecting up schematic lines and laying out a PCB is fairly straight forward.
I love the concept of schematics-as-code. But layout-as-code is awful. Maybe AI tools will improve it enough - but unless there's a user friendly escape hatch which lets you tweak things with a mouse, it's a dead end.
I've had the same problem with auto-generated wiring diagrams and flowcharts. Great in theory, but then the tool generates some ugly thing you can't make sense of and there's no way to change it.
There's definitely tremendous potential for AI in electronic design. But I think the really complicated bit is component selection and library management - you have to somehow keep track of component availability, size and a range of parameters which are hidden on page 7 of a PDF datasheet. Not to mention separate PDF application notes and land patterns.
Compared to all that, connecting up schematic lines and laying out a PCB is fairly straight forward.
Love that you tried the tool and couldn't agree more. We think that new layout paradigms similar to flex and CSS grid need to be invented for circuit layout. This is an active area of research for us!
I love this article. Two things I'd consider highlighting:
1. Autrouting in particular is a problem well-suited to visualization and not all problems are. it's fundamentally about real things in real space (bonus: they're mostly constrained to a 2D plane, which makes them easy to visualize on a computer screen). A lot of hard and interesting problems don't match that reality and so aren't as amenable to visualization.
(... but the general advice, "look for opportunities to visualize," stands. Your eyes and brain are very fast matchers for a broad variety of patterns in a way a computer isn't without a lot of special-purpose, tuned software).
2. JavaScript, between the language itself (specifically the ability to reflect data structures at runtime) and the ecosystem around it, is very amenable to visualization. In that way specifically, it was a strong choice for this problem domain. Imagine how much work you'd be taking on if you decided to "just bang out a quick visualization" for an arbitrary piece of the algorithm in C++, or Rust.
1. Autrouting in particular is a problem well-suited to visualization and not all problems are. it's fundamentally about real things in real space (bonus: they're mostly constrained to a 2D plane, which makes them easy to visualize on a computer screen). A lot of hard and interesting problems don't match that reality and so aren't as amenable to visualization.
(... but the general advice, "look for opportunities to visualize," stands. Your eyes and brain are very fast matchers for a broad variety of patterns in a way a computer isn't without a lot of special-purpose, tuned software).
2. JavaScript, between the language itself (specifically the ability to reflect data structures at runtime) and the ecosystem around it, is very amenable to visualization. In that way specifically, it was a strong choice for this problem domain. Imagine how much work you'd be taking on if you decided to "just bang out a quick visualization" for an arbitrary piece of the algorithm in C++, or Rust.
The third point - "Spatial Hash Indexing > Tree Data Structures" - is probably great in this domain, but shouldn't necessarily be taken as global truth. I've optimised a system significantly before by going from a grid to a tree - because if your points are significantly unevenly distributed then you effectively have a bad hash function, and it devolves to O(n) performance.
Not to mention that most hashmap implementations for managed languages incur ~2 cache misses per lookup. I've had an awful lot of optimisation wins over the years just by ripping hash maps out of software that either didn't actually need arbitrary key->value lookups, or had datasets large enough that a sufficiently wide tree-like structure handily beat the hashmap
I wonder how autorouters encode all of the various electronics rules. Such as min distance between traces, max angle of a turn, how to snap multiple connections together so they look good, etc.
As far as I know, the design rules are managed as net properties, and the typical auto router opinion of aesthetics is "lol." So a lot of effort that could be spent typing in rules and cleaning up aesthetics is often spent just routing the damn board manually.
I am a strong proponent of visualisations. I also have a tendency to write things in JavaScript that others might choose a different language. I'm now wondering if those things are related. Having the browser to provide an interface can make that a lot easier.
Recently I have been doing a fair bit of transformer and NNet stuff so consequently Python. I have found it a bit of a step back when it comes to visualisations. It's easy to used canned techniques to plot data, but not as easy to knock up a custom interactive visualisation.
Hmm, I wonder what python raylib is like, that might be the ticket.
Hmm, I wonder what python raylib is like, that might be the ticket.
Visualization is actually one of the reasons I've been successful with my current project (creating a 3D modeling library which uses 3D tool representations and movement as afforded by G-code to write out G-code or DXFs) --- the tool which makes it possible is:
https://pythonscad.org/
Being able to just plot something and see the algorithm on-screen has been an incredible benison.
I'm hopeful OpenPythonSCAD will be merged into OpenSCAD presently.
https://pythonscad.org/
Being able to just plot something and see the algorithm on-screen has been an incredible benison.
I'm hopeful OpenPythonSCAD will be merged into OpenSCAD presently.
So you are working on a way to generate toolpath gcode from python? I need that.
I have a python workflow for my laser cutter, I can make a drawing with drawsvg and then cut it with lightburn.
I have used build123d to make 3d models in python but not yet found a way to generate toolpaths for my milling machine.
I have a python workflow for my laser cutter, I can make a drawing with drawsvg and then cut it with lightburn.
I have used build123d to make 3d models in python but not yet found a way to generate toolpaths for my milling machine.
Yes.
Give the code a try!
https://github.com/WillAdams/gcodepreview
If you can describe a simple part I can work it up for you and add it to the examples.
EDIT: Alternately, try pyCAM, or Kiri:Moto w/ an STL?
Give the code a try!
https://github.com/WillAdams/gcodepreview
If you can describe a simple part I can work it up for you and add it to the examples.
EDIT: Alternately, try pyCAM, or Kiri:Moto w/ an STL?
I'd love to see what is the easiest way, for a hardcore backend guy with completely dumb part of brain responsible for visuals, to quickly visualize things in JS. I would pay for a "d3/p5 tutorial".
Author here. I would recommend booting up React Cosmos[1] and prompting Claude to "generate a react app that...". You can visualize inside of Claude then when you're finished, drop the output of Claude into React Cosmos as a file. This is great for prototyping algorithms and recording the progression.
For example you might say "generate an interactive react app that implements a simple pathfinder that uses A* with a penalty for going near edges". You can also just drop in a paper or pseudo code of an algorithm you're ideating on.
Here's an example how we use react cosmos for our autorouter[2]
[1] https://reactcosmos.org/docs [2] https://unraveller.vercel.app/?fixtureId=%7B%22path%22%3A%22...
For example you might say "generate an interactive react app that implements a simple pathfinder that uses A* with a penalty for going near edges". You can also just drop in a paper or pseudo code of an algorithm you're ideating on.
Here's an example how we use react cosmos for our autorouter[2]
[1] https://reactcosmos.org/docs [2] https://unraveller.vercel.app/?fixtureId=%7B%22path%22%3A%22...
This is why I use python with Jupyter notebooks.
Though admittedly animations are quite arduous.
Though admittedly animations are quite arduous.
Does anybody have materials regarding how you would go about writing an autorouter? I was thinking to maybe use something like this [0] as a starting point?
I have a few major nags about current autorouters:
1. No way to prioritise connections.
2. Handling of differential pairs or buses is terrible.
3. Does not use best practices for high-speed signal lines.
4. No way to add arbitrary rules to the autorouting process, i.e. block a trace from going through an area. For example, maybe you don't want a power trace being autorouted under your IMU, but some low voltage signals are fine.
I've use freerouting [1] in the past but it has a somewhat difficult time of being maintained. Freerouting seems to really struggle with larger designs, it seems to be greedily routing traces and spends ages trying to fix those early placed traces.
[0] https://github.com/vygr/Python-PCB
[1] https://github.com/freerouting/freerouting
I have a few major nags about current autorouters:
1. No way to prioritise connections.
2. Handling of differential pairs or buses is terrible.
3. Does not use best practices for high-speed signal lines.
4. No way to add arbitrary rules to the autorouting process, i.e. block a trace from going through an area. For example, maybe you don't want a power trace being autorouted under your IMU, but some low voltage signals are fine.
I've use freerouting [1] in the past but it has a somewhat difficult time of being maintained. Freerouting seems to really struggle with larger designs, it seems to be greedily routing traces and spends ages trying to fix those early placed traces.
[0] https://github.com/vygr/Python-PCB
[1] https://github.com/freerouting/freerouting
> > A recursive algorithm is a depth first search. Any loop that explores candidates/neighbors without sorting the candidates is a BFS.
BFS is also a recursive search. Even in the case of non-recursive search, the only difference is whether you use a queue or stack.
Apart from that, great article.
BFS is also a recursive search. Even in the case of non-recursive search, the only difference is whether you use a queue or stack.
Apart from that, great article.
And even then, execution depends on your language and compiler.
Write a recursive BFS in Haskell and it won’t blow up the stack.
Write a recursive BFS in Haskell and it won’t blow up the stack.
Any language with decent TCO won't do that. Python is the only big language that I can think of that doesn't do it.
Guaranteed TCO is pretty rare unfortunately.
Java, Go, JavaScript all lack it.
Java, Go, JavaScript all lack it.
Actually, you are right.
> 2. Implementation Language doesn’t matter
I'm not sure how performant modern day JS is but aren't you still leaving a lot of performance improvements on the table? Sure, algorithm may matter more but who says you can't use the same algorithm.
I'm not sure how performant modern day JS is but aren't you still leaving a lot of performance improvements on the table? Sure, algorithm may matter more but who says you can't use the same algorithm.
>I believe that solving autorouting will be a massive unlock for physical-world innovation and is a key piece to enable the “vibe-building” of electronics.
I strongly believe that the CAD world including EDA is at the verge of disruption by an AI or more correctly Machine Intelligence (Constraint Programming - CP to be exact) very similar to how LLM disrupting the Chatbot technology [1],[2].
The path to this is most probably by solving the autorouting mechanism with CP, a deterministic logic, optimization and constraint programming machine based intelligence [3], [4], [5],[6].
Fun facts, the modern founder of logic, optimization, and constraint programming is George Boole, the grandfather of Geoffrey Everest Hinton, the "Godfather of AI" and "Godfather of Deep Learning".
[1] Most AI value will come from broad automation, not from R & D (313 comments):
https://news.ycombinator.com/item?id=43447616
[2] Diagrams AI can, and cannot, generate (68 comments):
https://news.ycombinator.com/item?id=43398434
[3] Constraint programming:
https://en.wikipedia.org/wiki/Constraint_programming
[4] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:
https://www.youtube.com/live/TknN8fCQvRk
[5] "We Really Don't Know How to Compute!" - Gerald Sussman - MIT (2011) [video]:
https://youtube.com/watch?v=HB5TrK7A4pI
[6] High-Quality Ultra-Compact Grid Layout of Grouped Networks [PDF]:
https://ialab.it.monash.edu/~dwyer/papers/gridlayout2015.pdf
I strongly believe that the CAD world including EDA is at the verge of disruption by an AI or more correctly Machine Intelligence (Constraint Programming - CP to be exact) very similar to how LLM disrupting the Chatbot technology [1],[2].
The path to this is most probably by solving the autorouting mechanism with CP, a deterministic logic, optimization and constraint programming machine based intelligence [3], [4], [5],[6].
Fun facts, the modern founder of logic, optimization, and constraint programming is George Boole, the grandfather of Geoffrey Everest Hinton, the "Godfather of AI" and "Godfather of Deep Learning".
[1] Most AI value will come from broad automation, not from R & D (313 comments):
https://news.ycombinator.com/item?id=43447616
[2] Diagrams AI can, and cannot, generate (68 comments):
https://news.ycombinator.com/item?id=43398434
[3] Constraint programming:
https://en.wikipedia.org/wiki/Constraint_programming
[4] Logic, Optimization, and Constraint Programming: A Fruitful Collaboration - John Hooker - CMU (2023) [video]:
https://www.youtube.com/live/TknN8fCQvRk
[5] "We Really Don't Know How to Compute!" - Gerald Sussman - MIT (2011) [video]:
https://youtube.com/watch?v=HB5TrK7A4pI
[6] High-Quality Ultra-Compact Grid Layout of Grouped Networks [PDF]:
https://ialab.it.monash.edu/~dwyer/papers/gridlayout2015.pdf
This blog post sings to me. Well done. I remember the time a colleague effectively used A* as the optimizer for the AI's plans in 2 player game. Indeed it really can be purposed for a lot of different things!
I want to thank this guy for the "avoid Monte Carlo" point and the big "no randomness" image.
I feel people are getting way to comfortable throwing randomness at things, pretending it's perfectly fine or even beneficial because you can reason over the probability distributions and then shrugging their shoulders when the results have unpredictable and impossible to debug edge case behavior.
We already have enough unpredictable factors in a program, I feel we don't have to purposefully add even more of them.
I feel people are getting way to comfortable throwing randomness at things, pretending it's perfectly fine or even beneficial because you can reason over the probability distributions and then shrugging their shoulders when the results have unpredictable and impossible to debug edge case behavior.
We already have enough unpredictable factors in a program, I feel we don't have to purposefully add even more of them.
Agreed. Adding a test that generates a random 32 bit int to your project is adding 4 billion tests to your project and only running one of them.
If you're navigating on a 2D grid, Jump Point Search "Plus" can be extremely fast:
https://www.gameaipro.com/GameAIPro2/GameAIPro2_Chapter14_JP...
https://www.gameaipro.com/GameAIPro2/GameAIPro2_Chapter14_JP...
I find it very amusing how essentially all digital vlsi has been autorouted for years, but no one can seemingly solve the board level PCB autorouting problem. I get that one is digital, and the other can be essentially anything, but the difference in practice is a strange juxtaposition.
Don't diss monte carlo.
10000000000 random guesses can be way faster than a clever calculation.
And maybe there is no clever calculation.
10000000000 random guesses can be way faster than a clever calculation.
And maybe there is no clever calculation.
Agreed, if you aren't using Monte Carlo methods in your algorithms then your problem probably isn't hard enough, or your solutions are fragile to variance.
"Know A* like the back of your hand, use it everywhere"
Are state-of-the-art autorouters still based on A*?
From what I remember A* was used in the 80s before Specctra introduced a cost based push and shove capable approach in the mid 90s.
A* being what it is, is probably still a part of contemporary solutions, but I am surprised about the emphasis it got in the post.*
Are state-of-the-art autorouters still based on A*?
From what I remember A* was used in the 80s before Specctra introduced a cost based push and shove capable approach in the mid 90s.
A* being what it is, is probably still a part of contemporary solutions, but I am surprised about the emphasis it got in the post.*
Looking forward! Feature request: would it be possible to add obfuscation? This is not a solution to my issue ( it is more social), but could be… interesting
What a wonderfully written post! Much respect.
Wish i had read #3 a few years ago before doing one of those 'interview' 'projects'.
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I'm always curious about people entering the space though. It is a small market, IMO. The tools are fractured, the existing players are lumbering behemoths, and the users are cranky zealots (you will have to pry KiCad out of my cold dead hands). I noted the step about the AR being written in JavaScript, which I have no real opinion on, but I'm curious about plans to plug into ecosystems (CAD vendors, OS tools) or try and draw people into another new ecosystem.