There are also many high-performance Chinese implementations that are open-source (e.g., XuanTie C910, XiangShan, etc.).
While achieving an open-core design comparable to Zen 5 is unlikely in the near term, a sustained open-source collaborative effort could, in the long run, significantly change the situation. For example, current versions of XiangShan are targeting ~20 SPECint 2006/GHz (early where at ~9).
I suspect that many projects—such as BOOM—have stalled as a consequence of this situation. If it continues, the long-term impact will be highly detrimental for everyone involved, including stakeholders in Western countries.
This is going to end badly — and soon. It’s ironic that catastrophic forgetting (i.e., the inability to perform continuous learning) and hallucinations (i.e., the failure to recognize when a prediction is unfounded) won’t be the causes of the crash, but rather greed and stupidity.
As far as I know, pruning is related to age. At birth, we have a massive number of silent synapses. As we grow older, those that remain unused (i.e., inactive) tend to disappear. This process involves a delicate mechanism, including components of the immune system.
The unfortunate reality is that no one truly understands how memory works. Many theories are floating around, but the fundamental components remain elusive. One thing is certain: it is quite different from backpropagation. Thankfully, our brains do not suffer from catastrophic forgetting.
The issue is that no one fully understands why synaptic pruning occurs in biology. Large language models have no direct connection to biological systems, and pruning in LLMs is no exception.
The usefulness of AST is not limited to biological systems. A hypothetical conscious electronic system might also need AST to: (1) stabilize internal states more quickly during learning, (2) respond more quickly to input stimuli, (3) use internal predictive flows to 'simulate' inputs in order to learn from a reduced number of sensory-linked events.
It may not magically appear. It may, by design, be woven into the learning process.
Proof of that this is wrong, is that our brains require <20W of power. Non-brute force AI will be very efficient. Simple operations (int add/cmp) and no float matrix multiplications and keep moving around the same data all the time from memory to the ALU. GPUs will be overthrown by simple PIM.
My comment was not about the ISA implementation or specification, It's about the TCB (trusted compute base), which in Apple (like intel and AMD) is closed. In RiscV is open. I would recommend you to educate yourself on any topic before lecture others.
Security through obscurity is really a bad idea, and Apple is no exception. In the long run, this will likely drive the adoption of RiscV as a better alternative.
I wonder what will happen if someone figures out that fused FP mult add is no longer needed (e.g. just count spikes and add subtract permanence). This could be a big problem for the guys with all their eggs in one basket (like NVIDIA).