Matthew Thomas is a software engineer based in Sydney with eight years of experience building and optimizing back-end systems and machine learning workloads. Currently at Arm, he contributes to Arm NN, where his work enabled quantised 8-bit operations and streamlined CL tensor initialization for improved ML inference performance. He combines C++ systems programming with practical ML engineering, tackling build-system fixes and workload refactors that make models run faster and more maintainable. Colleagues appreciate that he focuses on low-level details that yield measurable performance gains, not just features—an approach evident in his contributions to a widely used Arm ML software mirror.
Arm NN ML Software. The code here is a read-only mirror of https://review.mlplatform.org/admin/repos/ml/armnn
Role in this project:
Back-end Developer & ML Engineer
Contributions:114 commits, 10 PRs, 2695 pushes in 4 years 4 months
Contributions summary:Matthew contributed to the Arm NN ML Software repository by implementing and enabling support for quantised addition operations, focusing on the backends. Their work included modifications to C++ source code, specifically within the ClWorkloads and ClFullyConnectedWorkload directories, indicating involvement in layer implementations and support. Furthermore, the user addressed a GitHub issue related to the CMake build system, by updating FindBoost.cmake and integrating support for 8-bit fully connected layers and also simplifying the initialization of Arm Compute CL Tensor data which is indicative of optimization work for ML models. The user also refactored code for workload management.
AArch32 and AArch64 Runtime Code Generation Library
Contributions:2 pushes, 1 branch in 3 years
code-generationruntimeaarch32aarch64quake
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