Scaling Analog Photonic Accelerators for Byte-Size, Integer General Matrix Multiply (GEMM) Kernels
<|reference_start|>Scaling Analog Photonic Accelerators for Byte-Size, Integer General Matrix Multiply (GEMM) Kernels: Deep Neural Networks (DNNs) predominantly rely on General Matrix Multiply (GEMM) kernels, which are often accelerated using specialized hardware architectures. Recently, analog photonic GEMM accelerators have emerged as a promising alternative, offering vastly superior speed and energy efficiency compared to traditional electronic accelerators. However, these photonic cannot support wider than 4-bit integer operands due to their inherent trade-offs between analog dynamic range and parallelism. This is often inadequate for DNN training as at least 8-bit wide operands are deemed necessary to prevent significant accuracy drops. To address these limitations, we introduce a scalable photonic GEMM accelerator named SPOGA. SPOGA utilizes enhanced features such as analog summation of homodyne optical signals and in-transduction positional weighting of operands. By employing an extended optical-analog dataflow that minimizes overheads associated with bit-sliced integer arithmetic, SPOGA supports byte-size integer GEMM kernels, achieving significant improvements in throughput, latency, and energy efficiency. Specifically, SPOGA demonstrates up to 14.4$\times$, 2$\times$, and 28.5$\times$ improvements in frames-per-second (FPS), FPS/Watt, and FPS/Watt/mm$^2$ respectively, compared to existing state-of-the-art photonic solutions.<|reference_end|>
@article{alo2024scaling,
    title={Scaling Analog Photonic Accelerators for Byte-Size, Integer General
  Matrix Multiply (GEMM) Kernels},
    author={Oluwaseun Adewunmi Alo, Sairam Sri Vatsavai, Ishan Thakkar},
    journal={arXiv preprint arXiv:2407.06134},
    year={2024},
    doi={10.1109/ISVLSI61997.2024.00080},
    archivePrefix={arXiv},
    eprint={2407.06134},
    primaryClass={cs.AR cs.ET cs.PF}
}