ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU151218" target="_blank" >RIV/00216305:26230/24:PU151218 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10650823" target="_blank" >https://ieeexplore.ieee.org/document/10650823</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN60899.2024.10650823" target="_blank" >10.1109/IJCNN60899.2024.10650823</a>

Result language

angličtina

Original language name

ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers

Original language description

Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy. In this work, we present ApproxDARTS, a neural architecture search (NAS) method enabling the popular differentiable neural architecture search method called DARTS to exploit approximate multipliers and thus reduce the power consumption of generated neural networks. We showed on the CIFAR-10 data set that the ApproxDARTS is able to perform a complete architecture search within less than 10 GPU hours and produce competitive convolutional neural networks (CNN) containing approximate multipliers in convolutional layers. For example, ApproxDARTS created a CNN showing an energy consumption reduction of (a) 53.84% in the arithmetic operations of the inference phase compared to the CNN utilizing the native 32-bit floating-point multipliers and (b) 5.97% compared to the CNN utilizing the exact 8-bit fixed-point multipliers, in both cases with a negligible accuracy drop. Moreover, the ApproxDARTS is 2.3 times faster than a similar but evolutionary algorithm-based method called EvoApproxNAS.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA24-10990S" target="_blank" >GA24-10990S: Hardware-Aware Machine Learning: From Automated Design to Innovative and Explainable Solutions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2024 The International Joint Conference on Neural Networks (IJCNN)

ISBN

979-8-3503-5931-2

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

1-8

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Yokohama

Event location

Yokohama

Event date

Jun 30, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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