Benoit Jacob
Fellow - AI Software at AMD
Old Toronto, Ontario, Canada
Summary
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RockstarBenoit Jacob is a Fellow in AI Software at AMD with 20 years of experience building high-performance compilers, numerical kernels, and tooling for machine learning and graphics. He has driven foundational work on low-precision and optimized matrix multiplication libraries (gemmlowp, ruy) and contributed performance-critical fixes across projects like XNNPACK, IREE, and LLVM/MLIR. At Google he led efforts that seeded TFLite and authored core ML inference primitives, and his open-source footprint spans everything from WebGL conformance tests to chemical and molecular tooling. Benoit pairs deep mathematical training with practical systems engineering—his early research background birthed the Eigen linear algebra side project that informed later work. Colleagues rely on him for subtle correctness fixes and architecture-aware micro-optimizations that materially improve throughput across CPU and mobile targets. He’s based in Old Toronto and known for turning brittle, platform-specific performance problems into robust, well-tested solutions.
19 years of coding experience
16 years of employment as a software developer
Github Skills (56)
webgl10
3d-rendering10
assembly10
c-language10
performance-analytics10
performance-monitor10
float3210
code-profiling10
artificial-neural-networks10
game-dev10
llvm10
testing10
matrix-multiplication10
cpu10
mlr10
Programming languages (14)
Github contributions (5)
google/gemmlowp
Jun 2015 - Dec 2019
Low-precision matrix multiplication
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:1 review, 409 commits, 177 PRs in 4 years 6 months
Contributions summary:Benoit's contributions primarily involve fixing bugs, adjusting and optimizing code related to matrix multiplication, and improving the performance of the gemmlowp library. The user focused on optimizing the handling of specific bit depths for operands, especially in the context of low-precision matrix multiplication. The commits also indicate work on improving the build process and adapting the code to support 64-bit ARM architecture, suggesting a focus on platform-specific optimizations.
precisionmatrix-multiplicationmatrixmultiplication
iree-org/iree
Apr 2020 - Jan 2023
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:1162 reviews, 221 commits, 1192 PRs in 2 years 9 months
Contributions summary:Benoit's commits primarily involved identifying and fixing performance bottlenecks within the IREE compiler, particularly in the context of matrix multiplication operations. They addressed issues related to incorrect implementations of matrix multiplication and also made changes to ensure the accuracy of floating point conversions. Moreover, they worked on optimizing the performance of kernels by implementing code improvements for the benefit of various CPU architectures. These optimizations included refactoring code and applying the principles of the existing architecture-specific code paths.
mlirspirvvulkantensorflowcompiler
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