HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
Article No.: 83, Pages 1 - 8
Abstract
The HyperDimensional Computing (HDC) Machine Learning (ML) paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other ML approaches, necessitating frameworks enabling fast HDC algorithm design space exploration. To this end, we introduce HDTorch, an open-source, PyTorch-based HDC library with CUDA extensions for hypervector operations. We demonstrate HDTorch's utility by analyzing four HDC benchmark datasets in terms of accuracy, runtime, and memory consumption, utilizing both classical and online HD training methodologies. We demonstrate average (training)/inference speedups of (111x/68x)/87x for classical/online HD, respectively. We also demonstrate how HDTorch enables exploration of HDC strategies applied to large, real-world datasets. We perform the first-ever HD training and inference analysis of the entirety of the CHB-MIT EEG epilepsy database. Results show that the typical approach of training on a subset of the data may not generalize to the entire dataset, an important factor when developing future HD models for medical wearable devices.
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- HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
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Information
Published In
October 2022
1467 pages
ISBN:9781450392174
DOI:10.1145/3508352
- Conference Chair:
- Tulika Mitra,
- Program Chairs:
- Evangeline Young,
- Jinjun Xiong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 December 2022
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- Research-article
Funding Sources
- ML-Edge Swiss National Science Foundation
- PEDESITE Swiss NSF Sinergia project
- European Union-Next-GenerationEU,University of Basque Country and the Spanish Ministry of Universities
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ICCAD '22
Sponsor:
ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design
October 30 - November 3, 2022
California, San Diego
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Overall Acceptance Rate 457 of 1,762 submissions, 26%
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