AI Chip Architectures

Those are amazing accomplishments but I am more interested in research developments in things like In-Memory (Analog) or other different approaches. Companies like EnCharge, Mythic, etc. And much more efficient devices like RRAM, MRAM, and FETs. Like FE-FETs with AlScN. The stuff just coming out of research or still in research is more exciting in terms of the potential for truly huge efficiency and performance boosts. Taalas is interesting also because of it's efficiency and speed. Guess it was just purchased by AMD.

Is anyone working on running neural networks on CPU architectures that are not limited by synchronous clock signals? Organic neural networks are inherently asynchronous and signals propagate through different parts of the network at their own pace.

I did not spot any errors in my skim of the architectures I'm familiar with where I would expect an LLM to go wrong, and it has a nicely scoped overview of topics that people in the space should familiarize themselves with. I don't think I've seen a better primer.

Maybe it's just me, but between the extremely thin font and layout design, I find this extremely difficult to parse. Overuse and improper use of italics is confusing as well.

I have a feeling the hardware architecture for LLMs are completely wrong. There's no way hundreds of kilowatts is required for intelligence.. just in terms of the physics. Is there someone out there in the analog/neuromorphic computing world that could make these power-hungry monsters completely redundant?