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Energy Efficient In-memory Hyperdimensional Encoding for Spatio-temporal Signal Processing
- Source :
- IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1725-1729, May 2021
- Publication Year :
- 2021
-
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.
- Subjects :
- Computer Science - Emerging Technologies
Subjects
Details
- Database :
- arXiv
- Journal :
- IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1725-1729, May 2021
- Publication Type :
- Report
- Accession number :
- edsarx.2106.11654
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TCSII.2021.3068126