Energy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing

TL;DR

This paper presents an in-memory hyperdimensional encoding architecture for spatio-temporal signal processing that significantly improves energy efficiency, area, and throughput while maintaining high classification accuracy.

Contribution

It introduces a novel in-memory computing architecture for hyperdimensional encoding of spatio-temporal data, enhancing efficiency and performance over traditional digital implementations.

Findings

1.80x energy efficiency gains

3.36x area reduction

9.74x throughput improvement

Abstract

The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks compared to the widely used deep learning-based approaches. Spatio-temporal (ST) signal processing, which encompasses biosignals such as electromyography (EMG) and electroencephalography (EEG), is one family of applications that could benefit from an HDC-based learning framework. At the core of HDC lie manipulations and comparisons of large bit patterns, which are inherently ill-suited to conventional computing platforms based on the von-Neumann architecture. In this work, we propose an architecture for ST signal processing within the HDC framework using predominantly in-memory compute arrays. In particular, we introduce a methodology for the in-memory hyperdimensional encoding of ST data to be used together with an in-memory associative search module. We show that the in-memory HDC encoder for ST signals offers at least 1.80x energy efficiency gains, 3.36x area gains, as well as 9.74x throughput gains compared with a dedicated digital hardware implementation. At the same time it achieves a peak classification accuracy within 0.04% of that of the baseline HDC framework.