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SupportHDC: Hyperdimensional Computing with Scalable Hypervector Sparsity

Authors:

Francky Catthoor,

Georges GielenAuthors Info & Claims

NICE '23: Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference

Pages 20 - 25

https://doi.org/10.1145/3584954.3584961

Published: 12 April 2023 Publication History

Abstract

Hyperdimensional Computing (HDC) is an emerging brain-inspired machine learning method that is recently gaining much attention for performing tasks such as pattern recognition and bio-signal classification with ultra-low energy and area overheads when implemented in hardware. HDC relies on the encoding of input signals into binary or few-bit Hypervectors (HVs) and performs low-complexity manipulations on HVs in order to classify the input signals. In this context, the sparsity of HVs directly impacts energy consumption, since the sparser the HVs, the more zero-valued computations can be skipped. This short paper introduces SupportHDC, a novel HDC design framework that can jointly optimize system accuracy and sparsity in an automated manner, in order to trade off classification performance and hardware implementation overheads. We illustrate the inner working of the framework on two bio-signal classification tasks: cancer detection and arrhythmia detection. We show that SupportHDC can reach a higher accuracy compared to the conventional splatter-code architectures used in many works, while enabling the system designer to choose the final design solution from the accuracy-sparsity trade-off curve produced by the framework. We release the source code for reproducing our experiments with the hope of being beneficial to future research.

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Cited By

Yi PYang YLee CAchour SEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)Early Termination for Hyperdimensional Computing Using Inferential StatisticsProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707254(342-360)Online publication date: 30-Mar-2025
https://dl.acm.org/doi/10.1145/3669940.3707254
Roodsari MKrautter JMeyers VTahoori M(2024) E 3 HDC: Energy Efficient Encoding for Hyper-Dimensional Computing on Edge Devices 2024 34th International Conference on Field-Programmable Logic and Applications (FPL)10.1109/FPL64840.2024.00045(274-280)Online publication date: 2-Sep-2024
https://doi.org/10.1109/FPL64840.2024.00045

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cover image ACM Other conferences

NICE '23: Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference

April 2023

124 pages

ISBN:9781450399470

DOI:10.1145/3584954

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 12 April 2023

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NICE 2023

NICE 2023: Neuro-Inspired Computational Elements Conference

April 11 - 14, 2023

TX, San Antonio, USA

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Cited By

Yi PYang YLee CAchour SEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)Early Termination for Hyperdimensional Computing Using Inferential StatisticsProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707254(342-360)Online publication date: 30-Mar-2025
https://dl.acm.org/doi/10.1145/3669940.3707254
Roodsari MKrautter JMeyers VTahoori M(2024) E 3 HDC: Energy Efficient Encoding for Hyper-Dimensional Computing on Edge Devices 2024 34th International Conference on Field-Programmable Logic and Applications (FPL)10.1109/FPL64840.2024.00045(274-280)Online publication date: 2-Sep-2024
https://doi.org/10.1109/FPL64840.2024.00045

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