"Energy-Efficient Recurrent Neural Network With MRAM-Based Probabilisti" by Shadi Sheikhfaal, Shaahin Angizi et al.
 

Energy-Efficient Recurrent Neural Network With MRAM-Based Probabilistic Activation Functions

Document Type

Article

Publication Date

4-1-2023

Abstract

Herein, we develop a programmable energy-efficient hardware implementation for Recurrent Neural Networks (RNNs) with Resistive Random-Access Memory (ReRAM) synapses and ultra-low power, area-efficient spin-based activation functions. To attain high energy-efficiency while maintaining accuracy, a novel Computing-in-Memory (CiM) architecture is proposed to leverage data-level parallelism during the evaluation phase. We employ an MRAM-based Adjustable Probabilistic Activation Function (APAF) via a low-power tunable activation mechanism, providing adjustable levels of accuracy to mimic ideal sigmoid and tanh thresholding along with a matching algorithm to regulate the neuron properties. Our hardware/software cross-layer simulation shows that our proposed design achieves up to 74.5× energy-efficiency with ∼11× area reduction compared to its counterpart designs while keeping the accuracy comparable.

Identifier

85137922115 (Scopus)

Publication Title

IEEE Transactions on Emerging Topics in Computing

External Full Text Location

https://doi.org/10.1109/TETC.2022.3202112

e-ISSN

21686750

First Page

534

Last Page

540

Issue

2

Volume

11

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