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ACE comes in by offering a technical standard that leverages the existing AVX10 registers but adds silicon dedicated to matrix multiplication. This brings multiple benefits, but the key advantages are better power efficiency, easier development and optimization, and leveraging AVX's 512-bit inputs. The latter makes for easy integration with existing designs by eschewing the need for ACE-specific inputs.
Matrix multiplication is the cornerstone of AI workloads: take a table of numbers, and run a multiplication-addition loop over the whole thing. This has always been possible with most any CPU, though at limited speed. Even today, running these loops uses a lot of power, even when leveraging x86's AVX10 multiply-accumulate instructions — something that's technically a hack, as AVX wasn't designed with 2D matrix operations multiplication in mind.
For the same number of input vectors, ACE can perform 16x as many operations, compared to AVX10. Note this doesn't necessarily mean a 16x speedup, as that will depend on each individual implementation, but it's reasonable to expect that Intel and AMD will dedicate more silicon to this task in future designs to improve performance. Plus, as each ACE instruction performs more work than its equivalent AVX10 loop, there's less CPU instruction overhead and potentially better RAM bandwidth usage right off the bat.
The benefits go far beyond just using fewer instructions for the same thing. ACE is intended to be implementation-agnostic, meaning that ML frameworks and their underlying libraries (PyTorch, TensorFlow) can just write one code path instead of having multiple variations depending on the underlying hardware and its degree of AVX support.
Demand for data center CPUs has surged, and AI agents are responsible
Key considerations
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Reference reading
- https://www.tomshardware.com/pc-components/cpus/SPONSORED_LINK_URL
- https://www.tomshardware.com/pc-components/cpus/intel-and-amds-new-ace-cpu-extensions-bring-an-efficient-ai-oriented-instruction-set-to-x86-a-new-design-makes-matrix-multiplication-more-power-and-density-efficient#main
- https://www.tomshardware.com/membership
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