Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Me...

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Bibliographic Details
Main Authors: Katie Spoon, Hsinyu Tsai, An Chen, Malte J. Rasch, Stefano Ambrogio, Charles Mackin, Andrea Fasoli, Alexander M. Friz, Pritish Narayanan, Milos Stanisavljevic, Geoffrey W. Burr
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Computational Neuroscience
Subjects:
analog accelerators
BERT
PCM
RRAM
in-memory computing
DNN
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full

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https://www.frontiersin.org/articles/10.3389/fncom.2021.675741/full

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