Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA
<|reference_start|>Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA: Modern Neural Network (NN) architectures heavily rely on vast numbers of multiply-accumulate arithmetic operations, constituting the predominant computational cost. Therefore, this paper proposes a high-throughput, scalable and energy efficient non-element-wise matrix multiplication unit on FPGAs as a basic component of the NNs. We firstly streamline inter-layer and intra-layer redundancies of MADDNESS algorithm, a LUT-based approximate matrix multiplication, to design a fast, efficient scalable approximate matrix multiplication module termed "Approximate Multiplication Unit (AMU)". The AMU optimizes LUT-based matrix multiplications further through dedicated memory management and access design, decoupling computational overhead from input resolution and boosting FPGA-based NN accelerator efficiency significantly. The experimental results show that using our AMU achieves up to 9x higher throughput and 112x higher energy efficiency over the state-of-the-art solutions for the FPGA-based Quantised Neural Network (QNN) accelerators.<|reference_end|>
@article{zhu2024fast,,
    title={Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication
  on FPGA},
    author={Xuqi Zhu, Huaizhi Zhang, JunKyu Lee, Jiacheng Zhu, Chandrajit Pal,
  Sangeet Saha, Klaus D. McDonald-Maier and Xiaojun Zhai},
    journal={arXiv preprint arXiv:2407.02362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02362},
    primaryClass={cs.AR cs.AI cs.LG}
}