Cases for analog mixed signal computing integrated circuits for deep neural networks

Mingoo Seok; Minhao Yang; Zhewei Jiang; Aurel A. Lazar; Jae Sun Seo

doi:10.1109/VLSI-DAT.2019.8742044

Cases for analog mixed signal computing integrated circuits for deep neural networks

Mingoo Seok, Minhao Yang, Zhewei Jiang, Aurel A. Lazar, Jae Sun Seo

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

5 Scopus citations

Abstract

Computing technology has been a backbone of our society. Its importance is hard to overemphasize. Today, we again confirm its extreme importance with recent advances in deep neural networks. Those emerging workloads impose an unprecedented amount of arithmetic complexity and data access beyond our existing computing systems can barely handle. Particularly, mobile and embedded computing systems will face a major challenge in achieving energy-efficient computing for truly enabling intelligent systems. In this talk, we will discuss the emerging analog and mixed-signal circuit techniques to improve energy efficiency. We will discuss two recent cases using such techniques, one on the speech recognition processor in hybrid analog and digital circuits and the other on the embedded SRAM circuits that support analog-mixed-signal in-memory (in-bitcell) computing for convolutional and deep neural networks.

Original language	English (US)
Title of host publication	2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728106557
DOIs	https://doi.org/10.1109/VLSI-DAT.2019.8742044
State	Published - Apr 2019
Event	2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019 - Hsinchu, Taiwan, Province of China Duration: Apr 22 2019 → Apr 25 2019

Publication series

Name	2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019

Conference

Conference	2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019
Country/Territory	Taiwan, Province of China
City	Hsinchu
Period	4/22/19 → 4/25/19

ASJC Scopus subject areas

Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Instrumentation
Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/VLSI-DAT.2019.8742044

Cite this

Seok, M., Yang, M., Jiang, Z., Lazar, A. A., & Seo, J. S. (2019). Cases for analog mixed signal computing integrated circuits for deep neural networks. In 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019 Article 8742044 (2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSI-DAT.2019.8742044

Cases for analog mixed signal computing integrated circuits for deep neural networks. / Seok, Mingoo; Yang, Minhao; Jiang, Zhewei et al.
2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8742044 (2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019).

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

Seok, M, Yang, M, Jiang, Z, Lazar, AA & Seo, JS 2019, Cases for analog mixed signal computing integrated circuits for deep neural networks. in 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019., 8742044, 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019, Hsinchu, Taiwan, Province of China, 4/22/19. https://doi.org/10.1109/VLSI-DAT.2019.8742044

Seok M, Yang M, Jiang Z, Lazar AA, Seo JS. Cases for analog mixed signal computing integrated circuits for deep neural networks. In 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8742044. (2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019). doi: 10.1109/VLSI-DAT.2019.8742044

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abstract = "Computing technology has been a backbone of our society. Its importance is hard to overemphasize. Today, we again confirm its extreme importance with recent advances in deep neural networks. Those emerging workloads impose an unprecedented amount of arithmetic complexity and data access beyond our existing computing systems can barely handle. Particularly, mobile and embedded computing systems will face a major challenge in achieving energy-efficient computing for truly enabling intelligent systems. In this talk, we will discuss the emerging analog and mixed-signal circuit techniques to improve energy efficiency. We will discuss two recent cases using such techniques, one on the speech recognition processor in hybrid analog and digital circuits and the other on the embedded SRAM circuits that support analog-mixed-signal in-memory (in-bitcell) computing for convolutional and deep neural networks.",

author = "Mingoo Seok and Minhao Yang and Zhewei Jiang and Lazar, {Aurel A.} and Seo, {Jae Sun}",

note = "Funding Information: Swiss National Science Foundation, Columbia University Research Initiatives in Science and Engineering, and Wei Family Private Foundation, in part supports the works in this paper. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

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AB - Computing technology has been a backbone of our society. Its importance is hard to overemphasize. Today, we again confirm its extreme importance with recent advances in deep neural networks. Those emerging workloads impose an unprecedented amount of arithmetic complexity and data access beyond our existing computing systems can barely handle. Particularly, mobile and embedded computing systems will face a major challenge in achieving energy-efficient computing for truly enabling intelligent systems. In this talk, we will discuss the emerging analog and mixed-signal circuit techniques to improve energy efficiency. We will discuss two recent cases using such techniques, one on the speech recognition processor in hybrid analog and digital circuits and the other on the embedded SRAM circuits that support analog-mixed-signal in-memory (in-bitcell) computing for convolutional and deep neural networks.

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Cases for analog mixed signal computing integrated circuits for deep neural networks

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