research-article

LeHDC: learning-based hyperdimensional computing classifier

Authors:

Xiaolin XuAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 1111 - 1116

https://doi.org/10.1145/3489517.3530593

Published: 23 August 2022 Publication History

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.

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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
Yu TWu BChen KZhang GLiu W(2025)LAHDC: Logic-Aggregation-Based Query for Embedded Hyperdimensional Computing AcceleratorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.342090544:1(119-129)Online publication date: Jan-2025
https://doi.org/10.1109/TCAD.2024.3420905
Narkthong NDuan SRen SXu XTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)MicroVSA: An Ultra-Lightweight Vector Symbolic Architecture-based Classifier Library for Always-On Inference on Tiny MicrocontrollersProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640374(730-745)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640374
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Published: 23 August 2022

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DAC '22: 59th ACM/IEEE Design Automation Conference

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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
Yu TWu BChen KZhang GLiu W(2025)LAHDC: Logic-Aggregation-Based Query for Embedded Hyperdimensional Computing AcceleratorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.342090544:1(119-129)Online publication date: Jan-2025
https://doi.org/10.1109/TCAD.2024.3420905
Narkthong NDuan SRen SXu XTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)MicroVSA: An Ultra-Lightweight Vector Symbolic Architecture-based Classifier Library for Always-On Inference on Tiny MicrocontrollersProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640374(730-745)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640374
Yu TWu BChen KZhang GLiu W(2024)AttnACQ: Attentioned-AutoCorrelation-Based Query for Hyperdimensional Associative MemoryIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2024.343456271:12(4984-4988)Online publication date: Dec-2024
https://doi.org/10.1109/TCSII.2024.3434562
Yu TWu BChen KZhang GLiu W(2024)Memristor-Based Approximate Query Architecture for In-Memory Hyperdimensional ComputingIEEE Transactions on Computers10.1109/TC.2024.344186173:11(2605-2618)Online publication date: Nov-2024
https://doi.org/10.1109/TC.2024.3441861
Yu TWu BChen KZhang GLiu W(2024)Fully Learnable Hyperdimensional Computing Framework With Ultratiny Accelerator for Edge-Side ApplicationsIEEE Transactions on Computers10.1109/TC.2023.333731673:2(574-585)Online publication date: Feb-2024
https://doi.org/10.1109/TC.2023.3337316
Chen TZhang JXu ZRedmond SLovell NLiu GWang C(2024)Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet PhotoplethysmographyIEEE Transactions on Biomedical Engineering10.1109/TBME.2024.337727071:8(2483-2494)Online publication date: Aug-2024
https://doi.org/10.1109/TBME.2024.3377270
Mejri MAmarnath CChatterjee A(2024)DeepER-HD: An Error Resilient HyperDimensional Computing Framework with DNN Front-End for Feature Selection2024 IEEE 25th Latin American Test Symposium (LATS)10.1109/LATS62223.2024.10534617(1-6)Online publication date: 9-Apr-2024
https://doi.org/10.1109/LATS62223.2024.10534617
Ma DHao CJiao X(2024)Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process2024 25th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED60706.2024.10528698(1-5)Online publication date: 3-Apr-2024
https://doi.org/10.1109/ISQED60706.2024.10528698
Chen TLiu YLiu GWang C(2024)Lightweight Multi-task Hyperdimensional Computing Framework Driven by Binary Neural Network for Sleep Apnea Detection2024 IEEE Biomedical Circuits and Systems Conference (BioCAS)10.1109/BioCAS61083.2024.10798240(1-5)Online publication date: 24-Oct-2024
https://doi.org/10.1109/BioCAS61083.2024.10798240
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