oneapi-src/oneDNN
Mar 2025 – Mar 2025
1 Month active
Languages Used
CC++
Technical Skills
ARM ArchitectureCPU OptimizationEmbedded SystemsMachine Learning LibrariesPerformance Engineering
manjam01
PROFILE
Manjam01
Manaal Jamadar developed ACL Layer Normalization support for AArch64 in the oneapi-src/oneDNN repository, focusing on enhancing inference performance and hardware compatibility. Leveraging C and C++ with expertise in ARM architecture and performance engineering, Manaal enabled stateless LayerNorm by updating the ACL minimum version and refactored the implementation to use the experimental CpuMeanStdDevNormalization operator. The work included adding tensor dimension and data type validation, introducing performance heuristics, and adapting codepaths for channel-last formats. This feature improved correctness, maintainability, and configuration validation, reflecting a deep understanding of embedded systems and machine learning libraries within a complex codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
406
Activity Months1
Your Network
486 people
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered ACL Layer Normalization support on AArch64 in oneDNN by enabling stateless LayerNorm through an ACL 25.02 minimum version update. Refactored to utilize the experimental CpuMeanStdDevNormalization operator, added tensor dimension and data type validation, introduced performance heuristics, and adapted the codepaths for channel-last formats and broader configuration validation. This work broadens hardware support, improves inference performance, and enhances correctness and maintainability.
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered ACL Layer Normalization support on AArch64 in oneDNN by enabling stateless LayerNorm through an ACL 25.02 minimum version update. Refactored to utilize the experimental CpuMeanStdDevNormalization operator, added tensor dimension and data type validation, introduced performance heuristics, and adapted the codepaths for channel-last formats and broader configuration validation. This work broadens hardware support, improves inference performance, and enhances correctness and maintainability.
March 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++
Technical Skills
ARM ArchitectureCPU OptimizationEmbedded SystemsMachine Learning LibrariesPerformance Engineering
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