LeHDC

Shijin Duan, Yejia Liu, Shaolei Ren, Xiaolin Xu

2022Proceedings of the 59th ACM/IEEE Design Automation Conference37 citationsDOI

Abstract

Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the training loss. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15% compared to the baseline HDC.

Topics & Concepts

Computer scienceInferenceHeuristicLimitingArtificial intelligenceMachine learningBaseline (sea)Artificial neural networkResource (disambiguation)Computer networkEngineeringGeologyMechanical engineeringOceanographyFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural ComputingNeural Networks and Reservoir Computing