
Jonathan Deakin enhanced AArch64 development workflows in the graphcore/pytorch-fork repository by building CI script improvements that enabled local development with custom ComputeLibrary directories, incremental builds, and full CPU-core utilization. Using Python scripting and DevOps practices, he optimized build processes to reduce iteration time and improve reliability for cross-architecture projects. In the uxlfoundation/oneDNN repository, Jonathan extended BrGEMM convolution support on AArch64 to leverage SVE_128 vector extensions, implementing runtime configuration and ISA checks to ensure compatibility and performance. His work demonstrated depth in ARM architecture, low-level C++ programming, and CPU optimization, addressing hardware-specific challenges in modern neural network pipelines.

August 2025: Delivered targeted performance improvement for the uxlfoundation/oneDNN project by extending BrGEMM Convolution support on AArch64 with SVE_128. Implemented enabling/configuration for SVE_128 usage, ensuring correct handling of SIMD widths and ISA compatibility to unlock optimized convolution paths on eligible hardware. This aligns with the roadmap to leverage modern ARM vector extensions and expand hardware coverage.
August 2025: Delivered targeted performance improvement for the uxlfoundation/oneDNN project by extending BrGEMM Convolution support on AArch64 with SVE_128. Implemented enabling/configuration for SVE_128 usage, ensuring correct handling of SIMD widths and ISA compatibility to unlock optimized convolution paths on eligible hardware. This aligns with the roadmap to leverage modern ARM vector extensions and expand hardware coverage.
Month: 2025-05 Key features delivered: - AArch64 CI Script Enhancements for Local Development and Parallel Builds in graphcore/pytorch-fork, enabling custom ComputeLibrary directories, incremental builds, and full CPU-core utilization to speed up local development and CI. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Improved developer productivity and CI throughput for AArch64 workflows; established scalable baseline for local development and parallel builds, reducing iteration time and enhancing reliability. Technologies/skills demonstrated: - CI scripting, cross-architecture (AArch64), incremental and parallel builds, build optimization, version-control traceability.
Month: 2025-05 Key features delivered: - AArch64 CI Script Enhancements for Local Development and Parallel Builds in graphcore/pytorch-fork, enabling custom ComputeLibrary directories, incremental builds, and full CPU-core utilization to speed up local development and CI. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Improved developer productivity and CI throughput for AArch64 workflows; established scalable baseline for local development and parallel builds, reducing iteration time and enhancing reliability. Technologies/skills demonstrated: - CI scripting, cross-architecture (AArch64), incremental and parallel builds, build optimization, version-control traceability.
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