Earlier quoted context omitted.
I think Intel SIMD (AVX-512) is still best-of-class for vector math. Run Length Encoded algorithms in column stores like SAP HANA are currently the key use-case (revenue/market wise). What I've learned since the release of Apple M1 is that ISA extensions for Matrix math, as incorporated in Apple's AMX, complement the Vector-centric SIMD ISA and are probably a key battle front for ML/DL. A good Vector/Matrix SIMD-like…
What you're talking about here is the client/inference side, not the server/training side of the ML/DL compute battle. The former is meaningful in a sense, but practically insignificant in relation to the latter. Almost all ML/DL compute GPU, CPU, or otherwise is on the server side of the equation, and if you're relying on vector CPU math for it, well good luck. Accelerators have a very clear space for themselves on…
Intel Acquires Artificial Intelligence Chipmaker Habana Labs [1]:
> Intel estimates the total addressable market (TAM) for AI silicon by 2024 will be greater than $25 billion, and within that, AI silicon in the data center is expected to be greater than $10 billion in the same timeframe.
Amazon EC2 instances powered by Habana Gaudi [2]:
> Up to 40% better price performance for deep learning models
Matrix CPU math is another plausible future; Intel, AMD, and/or ARM are positioned to drive such an ISA extension. Accelerators, coprocessors, FPGAs, and CPU ISA extensions all seem to be in play; it is too early to pick winners in this nascent market.
[1] https://newsroom.intel.com/news-releases/intel-ai-acquisitio...