[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

PQ-HDC: Projection-Based Quantization Scheme for Flexible and Efficient Hyperdimensional Computing

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations (AIAI 2021)

Abstract

Brain-inspired Hyperdimensional (HD) computing is an emerging technique for low-power/energy designs in many machine learning tasks. Recent works further exploit the low-cost quantized (bipolarized or ternarized) HD model and report dramatic improvements in energy efficiency. However, the quantization loss of HD models leads to a severe drop in classification accuracy. This paper proposes a projection-based quantization framework for HD computing (PQ-HDC) to achieve a flexible and efficient trade-off between accuracy and efficiency. While previous works exploit thresholding-quantization schemes, the proposed PQ-HDC progressively reduces quantization loss using a linear combination of bipolarized HD models. Furthermore, PQ-HDC allows quantization with flexible bit-width while preserving the computational efficiency of the Hamming distance computation. Experimental results on the benchmark dataset demonstrate that PQ-HDC achieves a 2.82% improvement in accuracy over the state-of-the-art method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 119.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 149.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 149.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Change history

  • 22 June 2021

    A correction has been published.

References

  1. Kanerva, P.: Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors. Cogn. Comput. 1(2), 139–159 (2009)

    Article  Google Scholar 

  2. Rahimi, A., et al.: Efficient biosignal processing using hyperdimensional computing: network templates for combined learning and classification of ExG signals. Proc. IEEE 107(1), 123–143 (2019)

    Article  MathSciNet  Google Scholar 

  3. Rahimi, A., et al.: Hyperdimensional computing for noninvasive brain–computer interfaces: blind and one-shot classification of EEG error-related potentials. In: Proceedings of the 10th ACM/EAI International Conference on Bio-Inspired Information and Communications Technologies (BICT), pp. 19–26 (2017)

    Google Scholar 

  4. Najafabadi, F.R., Rahimi, A., Kanerva, P., Rabaey, J.M.: Hyperdimensional computing for text classification. In: Design, Automation Test in Europe Conference Exhibition (DATE). University Booth (2016)

    Google Scholar 

  5. Imani, M., et al.: VoiceHD: hyperdimensional computing for efficient speech recognition. In: ICRC, pp. 1–6. IEEE (2017)

    Google Scholar 

  6. Imani, M., et al.: HDNA: energy-efficient dna sequencing using hyperdimensional computing. In: BHI, pp. 271–274. IEEE (2018)

    Google Scholar 

  7. Chang, E., et al.: Hyperdimensional computing-based multimodality emotion recognition with physiological signals. In: Proceedings of the IEEE International Symposium on AI for Circuits and Systems (AICAS), March 2019, pp. 137–141 (2019)

    Google Scholar 

  8. Horowitz, M.: Computing’s energy problem (and what we can do about it). In: IEEE International Solid-State Circuits Conference on Digest of Technical Papers (ISSCC), February 2014, pp. 10–14 (2014)

    Google Scholar 

  9. Imani, M., et al.: QuantHD: a quantization framework for hyperdimensional computing. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) (2019)

    Google Scholar 

  10. Chuang, Y.-C., Chang, C.-Y., Wu, A.-Y.A.: Dynamic hyperdimensional computing for improving accuracy-energy efficiency trade-offs. In: IEEE Workshop on Signal Processing Systems (SiPS). IEEE (2020)

    Google Scholar 

  11. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). http://yann.lecun.com/exdb/mnist/

  12. UCI ML repository. http://archive.ics.uci.edu/ml/datasets/ISOLET

Download references

Acknowledgments

This work was supported by the Ministry of Science and Technology of Taiwan under Grants MOST 109-2221-E-002-175 and MOST 110-2218-E-002-034 -MBK.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chi-Tse Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, CT., Chang, CY., Chuang, YC., Wu, AY.(. (2021). PQ-HDC: Projection-Based Quantization Scheme for Flexible and Efficient Hyperdimensional Computing. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79150-6_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79149-0

  • Online ISBN: 978-3-030-79150-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics