oneapi-src/oneDNN
Jan 2026 – Apr 2026
2 Months active
Languages Used
C++
Technical Skills
AArch64 architectureC++C++ developmentCPU architectureSIMD programmingalgorithm design
Jonathan Deakin
PROFILE
Jonathan Deakin
Jonathan Deakin contributed to performance-critical features and reliability improvements across the oneapi-src/oneDNN and graphcore/pytorch-fork repositories, focusing on AArch64 and ARM architectures. He developed and optimized BRGEMM convolution kernels, introduced SVE_128 vectorization, and enhanced CI scripts for local development, leveraging C++ and Python. His work included reducing code size, improving SIMD utilization, and addressing hardware-specific regressions, resulting in measurable speedups and stability gains on platforms like Graviton and Neoverse V2. Jonathan also resolved low-level bugs in matrix microkernels, demonstrating depth in assembly and CPU optimization, and consistently delivered robust, maintainable solutions for complex, hardware-accelerated workloads.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
12Total
Bugs
2
Commits
12
Features
5
Lines of code
1,064
Activity Months4
Your Network
1042 peopleSame Organization
@arm.com497
Work History
April 2026
1 Commits
Apr 1, 2026April 2026: Fixed an AArch64 brgemm microkernel bug related to temporary register handling, ensuring correct pointer management during matrix operations. This correctness fix eliminates sporadic computation errors in GEMM workloads on ARM, reducing production risk and QA time and improving platform reliability.
1 Commits
Apr 1, 2026April 2026: Fixed an AArch64 brgemm microkernel bug related to temporary register handling, ensuring correct pointer management during matrix operations. This correctness fix eliminates sporadic computation errors in GEMM workloads on ARM, reducing production risk and QA time and improving platform reliability.
April 2026
January 2026
9 Commits • 3 Features
Jan 1, 2026In January 2026, oneDNN on oneapi-src delivered substantial AArch64 performance and footprint optimizations, enhanced SVE-backed vectorization, and targeted fixes that improve reliability on backward paths. Key features delivered include: 1) AArch64 BRGEMM kernel performance and footprint reduction: removed branch target alignment, eliminated redundant f16 assert, and adopted zero-latency moves, reducing code size by ~10% and delivering ~0.5% real-world gains on Graviton 3/4 (commits 2a82e4c1665f4ac24cd234d53abf472f803390aa; 20a7fa9a83fd11d8e00237816e9672b2504470c5; a926b68f3fe4cf37c74f2a4d821d6fb8552653f2). 2) SVE-enabled vectorization and AArch64 optimizations: added internal cpu_isa_t::sve w/o vlen, cleaned eltwise SIMD width usage, and enabled n_bcast_1_load with 10-20% speedups on Neoverse V2, plus related refactors to broadcasting and GEMV (commits a3bddb3b52b7d0cb4e8c16a49eb4beb7ae2594c6; 47573dbeeac3f54149365a51333fb1be513ab79a; 52168220e26ef13edc8f9041e41e4f983e477388). 3) Neoverse V2 convolution performance optimization: reordered ACL ahead of jit_1x1:sve_128 to address a ~20% regression for larger convolutions (commit 3e6699ef0e872f7789acf1159929c1b01810c4b4). 4) AArch64 element-wise kernel backward pass correctness: fixed guard on backward-specific logic to ensure correct register usage during backward passes (commit 9de81ff8490cddbf415e5cfb7bcdda15444d6012).
January 2026
9 Commits • 3 Features
Jan 1, 2026In January 2026, oneDNN on oneapi-src delivered substantial AArch64 performance and footprint optimizations, enhanced SVE-backed vectorization, and targeted fixes that improve reliability on backward paths. Key features delivered include: 1) AArch64 BRGEMM kernel performance and footprint reduction: removed branch target alignment, eliminated redundant f16 assert, and adopted zero-latency moves, reducing code size by ~10% and delivering ~0.5% real-world gains on Graviton 3/4 (commits 2a82e4c1665f4ac24cd234d53abf472f803390aa; 20a7fa9a83fd11d8e00237816e9672b2504470c5; a926b68f3fe4cf37c74f2a4d821d6fb8552653f2). 2) SVE-enabled vectorization and AArch64 optimizations: added internal cpu_isa_t::sve w/o vlen, cleaned eltwise SIMD width usage, and enabled n_bcast_1_load with 10-20% speedups on Neoverse V2, plus related refactors to broadcasting and GEMV (commits a3bddb3b52b7d0cb4e8c16a49eb4beb7ae2594c6; 47573dbeeac3f54149365a51333fb1be513ab79a; 52168220e26ef13edc8f9041e41e4f983e477388). 3) Neoverse V2 convolution performance optimization: reordered ACL ahead of jit_1x1:sve_128 to address a ~20% regression for larger convolutions (commit 3e6699ef0e872f7789acf1159929c1b01810c4b4). 4) AArch64 element-wise kernel backward pass correctness: fixed guard on backward-specific logic to ensure correct register usage during backward passes (commit 9de81ff8490cddbf415e5cfb7bcdda15444d6012).
August 2025
1 Commits • 1 Features
Aug 1, 2025August 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.
1 Commits • 1 Features
Aug 1, 2025August 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
May 2025
1 Commits • 1 Features
May 1, 2025Month: 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.
May 2025
1 Commits • 1 Features
May 1, 2025Month: 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.
Activity
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Quality Metrics
Correctness95.8%
Maintainability86.6%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
AArch64 architectureARM ArchitectureC++C++ developmentCPU OptimizationCPU architectureContinuous IntegrationConvolutional Neural NetworksDevOpsLow-Level ProgrammingPython ScriptingSIMD InstructionsSIMD programmingalgorithm designassembly language
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
graphcore/pytorch-fork
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Continuous IntegrationDevOpsPython Scripting
uxlfoundation/oneDNN
Aug 2025 – Aug 2025
1 Month active
Languages Used
C++
Technical Skills
ARM ArchitectureCPU OptimizationConvolutional Neural NetworksLow-Level ProgrammingSIMD Instructions