Emerging Neural Workloads and Their Impact on Hardware

David Brooks, Martin M. Frank, Tayfun Gokmen, Udit Gupta, X. Sharon Hu, Shubham Jain, Ann Franchesca Laguna, Michael Niemier, Ian O'Connor, Anand Raghunathan, Ashish Ranjan, Dayane Reis, Jacob R. Stevens, Carole Jean Wu, Xunzhao Yin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider existing and emerging neural workloads, and what hardware accelerators might be best suited for said workloads. We begin with a discussion of analog crossbar arrays, which are known to be well-suited for matrix-vector multiplication operations that are commonplace in existing neural network models such as convolutional neural networks (CNNs). We highlight candidate crosspoint devices, what device and materials challenges must be overcome for a given device to be employed in a crossbar array for a computationally interesting neural workload, and how circuit and algorithmic optimizations may be employed to mitigate undesirable characteristics from devices/materials. We then discuss two emerging neural workloads. We first consider machine learning models for one- and few-shot learning tasks (i.e., where a network can be trained with just one or a few, representative examples of a given class). Notably crossbar-based architectures can be used to accelerate said models. Hardware solutions based on content addressable memory arrays will also be discussed. We then consider machine learning models for recommendation systems. Recommendation models, an emerging class of machine learning models, employ distinct neural network architectures that operate of continuous and categorical input features which make hardware acceleration challenging. We will discuss the open research challenges and opportunities within this space.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
EditorsGiorgio Di Natale, Cristiana Bolchini, Elena-Ioana Vatajelu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1462-1471
Number of pages10
ISBN (Electronic)9783981926347
DOIs
StatePublished - Mar 2020
Event2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 - Grenoble, France
Duration: Mar 9 2020Mar 13 2020

Publication series

NameProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020

Conference

Conference2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
CountryFrance
CityGrenoble
Period3/9/203/13/20

ASJC Scopus subject areas

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Emerging Neural Workloads and Their Impact on Hardware'. Together they form a unique fingerprint.

  • Cite this

    Brooks, D., Frank, M. M., Gokmen, T., Gupta, U., Hu, X. S., Jain, S., Laguna, A. F., Niemier, M., O'Connor, I., Raghunathan, A., Ranjan, A., Reis, D., Stevens, J. R., Wu, C. J., & Yin, X. (2020). Emerging Neural Workloads and Their Impact on Hardware. In G. Di Natale, C. Bolchini, & E-I. Vatajelu (Eds.), Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 (pp. 1462-1471). [9116435] (Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/DATE48585.2020.9116435