research-article

Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays

Authors:

Brian D. Hoskins,

Matthew W. Daniels,

Mark D. Stiles,

Gina C. AdamAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems, Volume 19, Issue 2

Article No.: 16, Pages 1 - 24

https://doi.org/10.1145/3577214

Published: 18 May 2023 Publication History

Abstract

The movement of large quantities of data during the training of a deep neural network presents immense challenges for machine learning workloads, especially those based on future functional memories deployed to store network models. As the size of network models begins to vastly outstrip traditional silicon computing resources, functional memories based on flash, resistive switches, magnetic tunnel junctions, and other technologies can store these new ultra-large models. However, new approaches are then needed to minimize hardware overhead, especially on the movement and calculation of gradient information that cannot be efficiently contained in these new memory resources. To do this, we introduce streaming batch principal component analysis (SBPCA) as an update algorithm. Streaming batch principal component analysis uses stochastic power iterations to generate a stochastic rank-k approximation of the network gradient. We demonstrate that the low-rank updates produced by streaming batch principal component analysis can effectively train convolutional neural networks on a variety of common datasets, with performance comparable to standard mini-batch gradient descent. Our approximation is made in an expanded vector form that can efficiently be applied to the rows and columns of crossbars for array-level updates. These results promise improvements in the design of application-specific integrated circuits based around large vector-matrix multiplier memories.

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Index Terms

Low-Rank Gradient Descent for Memory-Efficient Training of Deep In-Memory Arrays
1. Hardware
  1. Emerging technologies
    1. Analysis and design of emerging devices and systems
      1. Emerging architectures

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cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 19, Issue 2

April 2023

214 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3587888

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 18 May 2023

Online AM: 13 January 2023

Accepted: 30 November 2022

Revised: 27 September 2022

Received: 17 August 2021

Published in JETC Volume 19, Issue 2

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