Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing

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 1.80× energy efficiency gains, 3.36× area gains, as well as 9.74× 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.

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

“…Refer to Table 9 for the summary. [Eggimann et al, 2021] Hand gesture recognition From [Moin et al, 2018] dense binary binding; permutation; superposition binarized centroids approach from [Moin et al, 2018] [ Karunaratne et al, 2021a] Hand gesture recognition From [Rahimi et al, 2016a] dense binary binding; permutation; superposition binarized centroids approach from [Montagna et al, 2018] The EEG and iEEG signals…”

Robust Clustering Using Hyperdimensional Computing

“…and images [15][16][17] can be represented using HDC. Current research findings have demonstrated that HDC can achieve comparable performance with traditional machine learning techniques but support few-shot learning [18][19][20][21][22][23], high energy efficiency [24][25][26][27][28][29][30][31][32][33][34], and hardware acceleration [35][36][37]. HDC has wide applications that are not limited to supervised learning (e.g., classification [38][39][40] and regression [41]), unsupervised learning (e.g., clustering [42][43][44][45]), and even reasoning [46][47][48].…”

Comprehensive analysis on error-robustness of FeFET computation-in-memory for hyperdimensional computing

“…CiM with phase change RAM (PRAM) can perform training and inference of HDC. 12,13) Furthermore, FeFET-based local multiply and global accumulate (LM-GA) CiM for HDC has been proposed, which achieves fast HV encoding in training and small circuit area. 14) However, nonvolatile memory used in CiM often causes errors.…”