[1] The FFT Strikes Back: An Efficient Alternative to Self-Attention (168 comments):
There's been neural processing chips since before LLM craze [1].
[1]: https://en.wikipedia.org/wiki/Neural_processing_unit#History
A Versal AI Edge FPGA has a theoretical performance of 0.7TFLOPs just from the DSPs alone, while consuming less power than a Raspberry Pi 5 and this is ignoring the AI Engines, which are exactly the ASICs that you are talking about. They are more power efficient than GPUs, because they don't need to pretend to run multiple threads each with their own register files or hide memory latency by swapping warps. Their 2D NOC plus cascaded connections allow them to have a really high internal memory bandwidth in-between the tiles at low power.
What they are missing is processing in memory, specifically LPDDR-PIM for GEMV acceleration. The memory controllers simply can't deliver a memory bandwidth that is competitive with what Nvidia has and I'm talking about boards like Jetson Orin here.
Honestly I've asked different hardware researchers this question and they all seem to give different answers.
https://www.achronix.com/blog/accelerating-llm-inferencing-f...