PhotoHDC: An Electro-Photonic Accelerator for Hyperdimensional Computing

Hyperdimensional Computing (HDC), a brain-inspired Machine Learning (ML) technique, excels in edge computing due to its low resource requirement and is now advancing to handle even data-center workloads. By harnessing the advantages of photonics, high-speed and energy-efficient HDC training and inference can be achieved, fostering rapid HDC development. This paper introduces PhotoHDC, the first electro-photonic accelerator designed for HDC training and inference, supporting traditional, record-based, and graph encoding. Our microarchitecture employs Mach-Zehnder Modulators and photodetectors to enable high-speed modulation with efficient dataflows optimized for HDC operations. We demonstrate that PhotoHDC can achieve one to four orders of magnitude lower Energy-Delay Product (EDP) than leading electro-photonic Deep Neural Network (DNN) accelerators when running HDC training and inference on popular datasets. We also show that photonics can address the limitations of compute-in-memory-based HDC accelerators, achieving three orders of magnitude lower EDP on average for both training and inference.