A 5 μW Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing

Abstract

Hyperdimensional computing (HDC) is a braininspired 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 energyefficient standard cell memory (SCM), the design is easily crosstechnology mappable. It achieves extremely low power, 5 µW in typical applications, and an energy-efficiency improvement over the state-of-the-art (SoA) digital architectures of up to 3× in postlayout simulations for always-on wearable tasks such as EMG 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× 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 applied to the task of ball bearing fault detection.

HyperSeed: Unsupervised Learning with Vector Symbolic Architectures

“…VSA [12], [15], [18]- [20] is a computing framework providing methods of representing and manipulating concepts and their meanings in a high-dimensional space. VSA finds its applications in, for example, cognitive architectures [21], natural language processing [22]- [24], biomedical signal processing [1], [25], approximation of conventional data structures [26], [27], and for classification tasks such as gesture recognition [1], [28], cyber threat detection [29], physical activity recognition [30], fault isolation [31], [32]. Examples of efforts on using VSA for other than classification learning tasks are using data HVs for clustering [33]- [35], semi-supervised learning [36], collaborative privacy-preserving learning [37], [38], multi-task learning [39], [40], distributed learning [41], [42].…”