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GitHub - trevorpogue/algebraic-nnhw: AI acceleration using matrix multiplication with half the multiplications (github.com)

AI Acceleration machine learning Algorithm architecture Source Code AI and Machine Learning Artificial Intelligence

This GitHub repository presents transformative advancements in machine learning accelerator architectures through a novel algorithm, the Free-pipeline Fast Inner Product (FFIP), which demands nearly half the number of multiplier units for equivalent performance, trading multiplications for low-bitwidth additions. It includes complete source code for implementing the FFIP algorithm and architecture, aimed at enhancing the computational efficiency of ML accelerators.
Main Points
- FFIP Algorithm and ArchitectureThe repository delivers a novel algorithm (FFIP) alongside a hardware architecture that enhances the compute efficiency of ML accelerators by reducing the number of necessary multiplications.
- Applicability and Performance of FFIPThe FFIP algorithm is applicable across various machine learning model layers and has been shown to outperform existing solutions in throughput and compute efficiency.
- Comprehensive Source Code for ImplementationThe source code provides a comprehensive setup for implementation including a compiler, RTL descriptions, simulation scripts, and testbenches.
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