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 1.80x 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.

References in corpus (3)

• Mixed-Precision In-Memory Computing
• Mixed-precision deep learning based on computational memory
• HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics

Cited by in corpus (4)

• A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
• Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
• Robust Clustering using Hyperdimensional Computing
• HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration