Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs make it extremely challenging to design efficient silicon photonics-based systems for DNN inference and training. Hyperdimensional Computing (HDC) is an emerging, brain-inspired machine learning technique that enjoys several advantages over existing DNNs, including being lightweight, requiring low-precision operands, and being robust to noise introduced by the nonidealities in the hardware. For HDC, computing in-memory (CiM) approaches have been widely used, as CiM reduces the data transfer cost if the operands can fit into the memory. However, inefficient multi-bit operations, high write latency, and low endurance make CiM ill-suited for HDC. On the other hand, the existing electro-photonic DNN accelerators are inefficient for HDC because they are specifically optimized for matrix multiplication in DNNs and consume a lot of power with high-precision data converters. In this paper, we argue that photonic computing and HDC complement each other better than photonic computing and DNNs, or CiM and HDC. We propose PhotoHDC, the first-ever electro-photonic accelerator for HDC training and inference, supporting the basic, record-based, and graph encoding schemes. Evaluating with popular datasets, we show that our accelerator can achieve two to five orders of magnitude lower EDP than the state-of-the-art electro-photonic DNN accelerators for implementing HDC training and inference. PhotoHDC also achieves four orders of magnitude lower energy-delay product than CiM-based accelerators for both HDC training and inference.

翻译：近年来，基于硅光子学的计算已成为深度神经网络（DNN）领域替代基于CMOS计算的一种有前景的方案。然而，DNN的非线性操作和高精度要求使得设计用于DNN推理和训练的高效硅光子学系统极具挑战性。超维计算（HDC）是一种新兴的、受大脑启发的机器学习技术，与现有DNN相比具有若干优势，包括轻量化、对操作数精度要求低，以及对硬件非理想性引入的噪声具有鲁棒性。对于HDC，存内计算（CiM）方法已被广泛采用，因为如果操作数能放入内存，CiM可以降低数据传输成本。然而，低效的多比特操作、高写入延迟和低耐久性使得CiM并不适合HDC。另一方面，现有的电-光DNN加速器对HDC而言效率低下，因为它们专门针对DNN中的矩阵乘法进行了优化，并且高精度数据转换器功耗巨大。在本文中，我们认为光子计算与HDC的互补性优于光子计算与DNN，或CiM与HDC。我们提出了PhotoHDC，这是首个用于HDC训练和推理的电-光加速器，支持基础的、基于记录的以及图编码方案。使用流行数据集进行评估，我们表明，在实现HDC训练和推理时，我们的加速器能够比最先进的电-光DNN加速器实现低二到五个数量级的能量延迟积（EDP）。同时，对于HDC训练和推理，PhotoHDC相比基于CiM的加速器也实现了低四个数量级的能量延迟积。