CAP-HDC: A CAM-Based Processor for Hyperdimensional Computing

作者：Yuhan He, Anqin Xiao, Tao Hu, Fanxi Yang, Hengtan Zhang, Lirong Zheng, Zhuo Zou · 年份：2025 · DOI：10.1109/iscas56072.2025.11043379 · 被引用次数：3 · 研究领域：Distributed and Parallel Computing Systems、Ferroelectric and Negative Capacitance Devices、Parallel Computing and Optimization Techniques

This paper presents CAP-HDC, a Content Addressable Memory (CAM)-based processor designed for Hyperdimensional Computing (HDC). CAP-HDC integrates the binding, bundling, permutation, and similarity operators of HDC into the in-memory associative processing framework that features high parallelism, thereby achieving low power consumption and latency. The CAM utilized in CAP-HDC is designed using the Split Word Lines (SWL) 6T bit cell, enabling column-wise searching across all rows simultaneously. The approximate bundling method is proposed to implement bundling in CAM with multiple Hypervectors (HVs) without sacrificing accuracy on the 5-Class Gesture dataset. The hierarchical permutation method is proposed to implement permutation with lower power consumption, achieving a reduction of 90.66% in power consumption compared to the direct circular shift method. CAP-HDC is simulated using the 22 nm CMOS process, occupying an area of 1.06 mm2 and consuming 1.08 mW at a clock frequency of 200 MHz with a 0.9 V power supply. Compared to previous works, CAP-HDC improves energy efficiency by 2.9x and latency by 2.4x on the MNIST dataset. For hand gesture prediction based on EMG signals, CAP-HDC achieves improvements of 3.1x in inference energy efficiency and 2.6x in encoding energy efficiency.