oneapi-src/oneDNN
Apr 2025 – Jun 2025
3 Months active
Languages Used
C++Shell
Technical Skills
CPU OptimizationJIT CompilationPerformance EngineeringTestingARM AssemblyFloating-Point Arithmetic
karmeh01
PROFILE
Karmeh01
Over a three-month period, contributed to oneapi-src/oneDNN by developing and optimizing features for ARM-based architectures, focusing on performance-critical paths. Delivered JIT FP16-to-FP16 reorder support on aarch64, updating kernel-level C++ code and expanding test coverage to improve inference throughput for deep learning workloads. Enhanced the JIT element-wise path by implementing FP16 support through FP16-to-FP32 conversion and back, leveraging ARM Assembly and floating-point arithmetic for efficient computation. Extended broadcasted grouped matrix multiplication with SVE_128 support, introducing new format tags and compatibility logic. Demonstrated expertise in CPU optimization, SIMD, and embedded systems, consistently delivering feature-focused, low-level engineering solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
96
Activity Months3
Your Network
617 peopleSame Organization
@arm.com538
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key deliverables, impact, and skills demonstrated for oneapi-src/oneDNN.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key deliverables, impact, and skills demonstrated for oneapi-src/oneDNN.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for oneDNN focusing on FP16 enhancements in the aarch64 JIT path. Delivered FP16 support for aarch64 element-wise operations by adding a FP16->FP32->compute->FP16 data path in the Just-In-Time compiler, expanding FP16 compute usability on ARM and enabling higher-performance inference for half-precision workloads.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for oneDNN focusing on FP16 enhancements in the aarch64 JIT path. Delivered FP16 support for aarch64 element-wise operations by adding a FP16->FP32->compute->FP16 data path in the Just-In-Time compiler, expanding FP16 compute usability on ARM and enabling higher-performance inference for half-precision workloads.
April 2025
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Delivered JIT FP16-to-FP16 reorder on aarch64 for oneDNN by updating the jit_uni_reorder_kernel_f32_t to support FP16->FP16 reordering and adding FP16 reorder test coverage. No major bugs fixed this month; feature-focused work with immediate impact on ARM64 FP16 performance. Business value: faster FP16 data path improves inference throughput for DNN workloads on ARM64 and strengthens reliability with dedicated tests. Technologies/skills demonstrated: JIT compilation, aarch64 optimization, FP16 data path, kernel-level C++ development, and test automation.
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Delivered JIT FP16-to-FP16 reorder on aarch64 for oneDNN by updating the jit_uni_reorder_kernel_f32_t to support FP16->FP16 reordering and adding FP16 reorder test coverage. No major bugs fixed this month; feature-focused work with immediate impact on ARM64 FP16 performance. Business value: faster FP16 data path improves inference throughput for DNN workloads on ARM64 and strengthens reliability with dedicated tests. Technologies/skills demonstrated: JIT compilation, aarch64 optimization, FP16 data path, kernel-level C++ development, and test automation.
April 2025
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Shell
Technical Skills
ARMARM AssemblyCPU ArchitectureCPU OptimizationEmbedded SystemsFloating-Point ArithmeticJIT CompilationLow-Level OptimizationOptimizationPerformance EngineeringSIMDTesting
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