RSR
Efficient Binary and Ternary Matrix Multiplication for Neural Network Acceleration
RSR (Rapid Sparse Representation) is an efficient binary and ternary matrix multiplication method designed to accelerate model inference time in neural networks. This project focuses on optimizing computational efficiency for quantized neural networks.
Key Achievements
- 24x speedup compared to the standard NumPy baseline
- 2.5x improvement on Quantized LLMs
- Published in ICML 2025 (International Conference on Machine Learning)
Technical Innovation
RSR implements novel algorithms for binary and ternary matrix multiplication that significantly reduce computational overhead while maintaining model accuracy. The method is particularly effective for quantized neural networks where traditional floating-point operations are replaced with more efficient binary and ternary operations.
Performance Impact
The dramatic speedup achieved by RSR makes it particularly valuable for:
- Edge computing applications with limited computational resources
- Real-time inference scenarios requiring low latency
- Large-scale deployment of quantized neural networks
Research Context
This work is part of my research on efficient AI systems and algorithmic optimization, contributing to the broader goal of making AI more accessible and deployable in resource-constrained environments.