K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM

Jyotishman Saikia; Shihui Yin; Zhewei Jiang; Mingoo Seok; Jae Sun Seo

doi:10.1109/ISLPED.2019.8824822

K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM

Jyotishman Saikia, Shihui Yin, Zhewei Jiang, Mingoo Seok, Jae Sun Seo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

The k-nearest neighbor (kNN) is one of the most popular algorithms in machine learning owing to its simplicity, versatility, and implementation viability without any assumptions about the data. However, for large-scale data, it incurs a large amount of memory access and computational complexity, resulting in long latency and high power consumption. In this paper, we present a kNN hardware accelerator in 65nm CMOS. This accelerator combines in-memory computing SRAM that is recently developed for binarized deep neural networks and digital hardware that performs top-k sorting. We designed and simulated the kNN accelerator, which performs up to 17.9 million query vectors per second while consuming 11.8 mW, demonstrating >4.8X energy improvement over prior works.

Original language	English (US)
Title of host publication	International Symposium on Low Power Electronics and Design, ISLPED 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728129549
DOIs	https://doi.org/10.1109/ISLPED.2019.8824822
State	Published - Jul 2019
Event	2019 IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2019 - Lausanne, Switzerland Duration: Jul 29 2019 → Jul 31 2019

Publication series

Name	Proceedings of the International Symposium on Low Power Electronics and Design
Volume	2019-July
ISSN (Print)	1533-4678

Conference

Conference	2019 IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2019
Country/Territory	Switzerland
City	Lausanne
Period	7/29/19 → 7/31/19

Keywords

content addressable memory
hardware accelerator
in-memory computing
k-nearest neighbor

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ISLPED.2019.8824822

Cite this

Saikia, J., Yin, S., Jiang, Z., Seok, M., & Seo, J. S. (2019). K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM. In International Symposium on Low Power Electronics and Design, ISLPED 2019 Article 8824822 (Proceedings of the International Symposium on Low Power Electronics and Design; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISLPED.2019.8824822

K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM. / Saikia, Jyotishman; Yin, Shihui; Jiang, Zhewei et al.
International Symposium on Low Power Electronics and Design, ISLPED 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8824822 (Proceedings of the International Symposium on Low Power Electronics and Design; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saikia, J, Yin, S, Jiang, Z, Seok, M & Seo, JS 2019, K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM. in International Symposium on Low Power Electronics and Design, ISLPED 2019., 8824822, Proceedings of the International Symposium on Low Power Electronics and Design, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2019, Lausanne, Switzerland, 7/29/19. https://doi.org/10.1109/ISLPED.2019.8824822

Saikia J, Yin S, Jiang Z, Seok M, Seo JS. K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM. In International Symposium on Low Power Electronics and Design, ISLPED 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8824822. (Proceedings of the International Symposium on Low Power Electronics and Design). doi: 10.1109/ISLPED.2019.8824822

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K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM

Abstract

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Keywords

ASJC Scopus subject areas

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