Tencent/ncnn
Apr 2026 – May 2026
2 Months active
Languages Used
C++
Technical Skills
C++ developmentEmbedded systemsNeural network optimizationRISC-V architectureC++Embedded Systems
Justin Fung
PROFILE
Justin Fung
Developed advanced quantization support for neural network inference on RISC-V architectures within the Tencent/ncnn repository, focusing on edge device performance and portability. Over two months, implemented a RISC-V Quantization Layer supporting both standard and FP16 formats, followed by RVV-accelerated Dequantize and Requantize layers optimized for integer and half-precision data types. Leveraged C++ and parallel programming techniques to enhance inference speed and memory efficiency, while maintaining compatibility with existing quantization graphs and tooling. The work established a robust foundation for cross-architecture optimization, enabling efficient deployment of quantized machine learning models on RISC-V vector-enabled embedded systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,963
Activity Months2
Your Network
46 peopleWork History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 focused on advancing quantization support on edge devices by delivering RVV-accelerated layers in Tencent/ncnn. Implemented two new layers, Dequantize and Requantize, optimized for RISC-V vector architectures. Dequantize supports integer and FP16 data types to enable efficient processing of quantized networks; Requantize supports ReLU and Leaky ReLU activations for post-quantization scaling and activation. These additions enhance performance and flexibility of quantized inference on RVV-enabled platforms, while maintaining compatibility with existing quantization graphs and tooling.
2 Commits • 1 Features
May 1, 2026May 2026 focused on advancing quantization support on edge devices by delivering RVV-accelerated layers in Tencent/ncnn. Implemented two new layers, Dequantize and Requantize, optimized for RISC-V vector architectures. Dequantize supports integer and FP16 data types to enable efficient processing of quantized networks; Requantize supports ReLU and Leaky ReLU activations for post-quantization scaling and activation. These additions enhance performance and flexibility of quantized inference on RVV-enabled platforms, while maintaining compatibility with existing quantization graphs and tooling.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented RISC-V Quantization Layer for Neural Network Inference in Tencent/ncnn, delivering quantization support for RVV 1.0 with standard and FP16 formats. This feature enhances performance and memory utilization on RISC-V edge devices, enabling efficient neural network inference and expanding hardware portability. The work lays groundwork for further quantization strategies and cross-architecture optimization, contributing to faster deployment and lower operational costs.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented RISC-V Quantization Layer for Neural Network Inference in Tencent/ncnn, delivering quantization support for RVV 1.0 with standard and FP16 formats. This feature enhances performance and memory utilization on RISC-V edge devices, enabling efficient neural network inference and expanding hardware portability. The work lays groundwork for further quantization strategies and cross-architecture optimization, contributing to faster deployment and lower operational costs.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ developmentEmbedded SystemsEmbedded systemsMachine LearningNeural network optimizationRISC-V architecturemachine learningparallel programming
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