ABSTRACT

Recently, brain-inspired hyperdimensional computing (HDC) has demonstrated promising capability in a wide range of applications such as medical diagnosis, human activity recognition, and voice classification, etc. Despite the growing popularity of HDC, its memory-centric computing characteristics make the associative memory implementation under significant energy consumption due to the massive data storage and processing. In this paper, we present a systematic case study to leverage the application-level error resilience of HDC to reduce the energy consumption of HDC associative memory by using voltage scaling. Evaluation results on various applications show that our proposed approach can achieve 47.6% energy saving on associative memory with a ≤% accuracy loss. We further explore two low-cost error masking methods: word masking and bit masking, to mitigate the impact of voltage scalinginduced errors. Experimental results show that the proposed word masking (bit masking) method can further enhance energy saving up to 62.3% (72.5%) with accuracy loss ≤1%.

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