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A 5 μ W Standard Cell Memory-Based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing.
- Source :
-
IEEE Transactions on Circuits & Systems. Part I: Regular Papers . Oct2021, Vol. 68 Issue 10, p4116-4128. 13p. - Publication Year :
- 2021
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Abstract
- Hyperdimensional computing (HDC) is a brain-inspired computing paradigm-based on high-dimensional holistic representations of vectors. It recently gained attention for embedded smart sensing due to its inherent error-resiliency and suitability to highly parallel hardware implementations. In this work, we propose a programmable all-digital CMOS implementation of a fully autonomous HDC accelerator for always-on classification in energy-constrained sensor nodes. By using energy-efficient standard cell memory (SCM), the design is easily cross-technology mappable. It achieves extremely low power, 5 $\mu \text{W}$ in typical applications, and an energy efficiency improvement over the state-of-the-art (SoA) digital architectures of up to $3\times $ in post-layout simulations for always-on wearable tasks such as Electromyography (EMG) hand gesture recognition. As part of the accelerator’s architecture, we introduce novel hardware-friendly embodiments of common HDC-algorithmic primitives, which results in $3.3\times $ technology scaled area reduction over the SoA, achieving the same accuracy levels in all examined targets. The proposed architecture also has a fully configurable datapath using microcode optimized for HDC stored on an integrated SCM-based configuration memory, making the design “general-purpose” in terms of HDC algorithm flexibility. This flexibility allows usage of the accelerator across novel HDC tasks, for instance, a newly designed HDC-algorithm for the task of ball bearing fault detection. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 68
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 153763143
- Full Text :
- https://doi.org/10.1109/TCSI.2021.3100266