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krishnasai-mcw

PROFILE

Krishnasai-mcw

Krishna Sai contributed to the uxlfoundation/oneDNN and oneapi-src/oneDNN repositories by engineering hardware-optimized deep learning primitives for RISC-V architectures. Over three months, Krishna delivered vectorized average pooling, multithreaded pooling, and 32-bit floating-point layer normalization, leveraging C++ and RVV intrinsics to maximize performance on RV64 platforms. His work included dynamic build system enhancements using cmake and compiler flags to enable architecture-aware code generation, as well as robust kernel refactoring to address edge-case reliability. By extending matrix multiplication with bias and ReLU post-ops, Krishna improved both performance and maintainability, demonstrating depth in CPU optimization and embedded systems development.

Overall Statistics

Feature vs Bugs

80%Features

Repository Contributions

9Total
Bugs
1
Commits
9
Features
4
Lines of code
1,685
Activity Months3

Work History

January 2026

1 Commits • 1 Features

Jan 1, 2026

January 2026 (2026-01) Monthly Summary for oneDNN (oneapi-src/oneDNN). Delivered 32-bit Floating-Point Layer Normalization on RISC-V with RVV, including the forward execution path and vectorized optimizations. No major bugs fixed this month. The work extends hardware support for DL workloads on RVV-enabled platforms and demonstrates cross-architecture performance tuning for CPU backends.

August 2025

4 Commits • 1 Features

Aug 1, 2025

Monthly summary for 2025-08 highlighting the UXfoundation/oneDNN work focused on expanding hardware acceleration paths and improving code quality. Key outcome: RVV-based matmul with bias and ReLU post-ops delivered, with robustness improvements, licensing hygiene, and maintainable code.

June 2025

4 Commits • 2 Features

Jun 1, 2025

June 2025 monthly summary for uxlfoundation/oneDNN focused on RISCV optimizations and reliability improvements for vector-enabled workloads. Delivered architecture-aware build and kernel enhancements leveraging RVV intrinsics to maximize performance on supported hardware, along with robust fixes to ensure stability in edge-case scenarios. This period emphasizes business value through faster inference, better hardware utilization, and higher reliability on RISCV deployments.

Activity

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Quality Metrics

Correctness90.0%
Maintainability84.4%
Architecture85.6%
Performance88.8%
AI Usage20.0%

Skills & Technologies

Programming Languages

CC++cmake

Technical Skills

Build SystemsC++CPU OptimizationCPU optimizationCode RefactoringDeep Learning OptimizationDeep learning frameworksEmbedded SystemsMatrix MultiplicationMultithreadingPerformance EngineeringRISC-VRISC-V architectureVector Extensions (RVV)Vector Instructions

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

uxlfoundation/oneDNN

Jun 2025 Aug 2025
2 Months active

Languages Used

C++cmakeC

Technical Skills

CPU OptimizationDeep Learning OptimizationEmbedded SystemsMultithreadingPerformance EngineeringRISC-V

oneapi-src/oneDNN

Jan 2026 Jan 2026
1 Month active

Languages Used

C++

Technical Skills

CPU optimizationDeep learning frameworksRISC-V architectureVector programming