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：近几年来，基于硅光子学的计算已成为深度神经网络领域替代CMOS计算的有前景方案。然而，深度神经网络所需的非线性运算和高精度要求，使得设计用于深度神经网络推理与训练的高效硅光子学系统极具挑战性。超维计算作为一种新兴的脑启发式机器学习技术，相比现有深度神经网络具有显著优势，包括轻量化、低精度操作数需求，以及对硬件非理想性引入噪声的鲁棒性。在超维计算中，存内计算方案被广泛采用，因为当操作数适配内存时，该方案能降低数据传输成本。但低效的多比特操作、高写入延迟和低耐久性导致存内计算难以满足超维计算需求。另一方面，现有电光深度神经网络加速器因专门针对深度神经网络中的矩阵乘法优化，且需高精度数据转换器而能耗较高，故不适用于超维计算。本文论证表明，光子计算与超维计算之间的互补性优于光子计算与深度神经网络或存内计算与超维计算。我们提出PhotoHDC——首个支持超维计算训练与推理的电光加速器，可兼容基础编码、记录编码和图编码方案。通过使用主流数据集进行评估，结果显示：在实现超维计算训练与推理时，本加速器的能量延迟积比现有最先进的电光深度神经网络加速器低2至5个数量级；同时，相比基于存内计算的加速器，PhotoHDC在超维计算训练与推理中的能量延迟积降低了4个数量级。