oneapi-src/oneDNN
Apr 2026 – Apr 2026
1 Month active
Languages Used
C++
Technical Skills
JIT compilationRISC-V architectureconvolutional neural networkshigh-performance computing
Alexandre de Limas Santana
PROFILE
Alexandre De Limas Santana
Developed a vector-length-agnostic JIT 1x1 convolution kernel for the oneapi-src/oneDNN repository, targeting RISC-V Vector (RVV) architectures to enable portable and efficient performance across varying vector lengths. The work involved implementing new format tags and updating memory descriptor handling in C++, ensuring correct data layout and memory access patterns for convolutional neural networks. By focusing on JIT compilation and high-performance computing techniques, the developer improved throughput for typical CNN workloads and enhanced the scalability and maintainability of both the kernel and its supporting memory model, addressing the need for broader RVV portability in deep learning frameworks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
213
Activity Months1
Your Network
94 peopleSame Organization
@bsc.es16
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for oneDNN: Delivered a vector-length-agnostic JIT 1x1 convolution kernel for RVV, enabling portable performance across RVV vector lengths and laying groundwork for scalable maintainable code. Implemented new format tags and updated memory descriptor handling to fully support the kernel, resulting in improved efficiency on diverse hardware profiles. The work enhances throughput for typical CNN scenarios and contributes to broader RVV portability and maintainability of the kernel and memory model.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for oneDNN: Delivered a vector-length-agnostic JIT 1x1 convolution kernel for RVV, enabling portable performance across RVV vector lengths and laying groundwork for scalable maintainable code. Implemented new format tags and updated memory descriptor handling to fully support the kernel, resulting in improved efficiency on diverse hardware profiles. The work enhances throughput for typical CNN scenarios and contributes to broader RVV portability and maintainability of the kernel and memory model.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
JIT compilationRISC-V architectureconvolutional neural networkshigh-performance computing
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