Evolutionary Neural Architecture Search Supporting Approximate Multipliers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU138875" target="_blank" >RIV/00216305:26230/21:PU138875 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-72812-0_6" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-72812-0_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-72812-0_6" target="_blank" >10.1007/978-3-030-72812-0_6</a>

Result language

angličtina

Original language name

Evolutionary Neural Architecture Search Supporting Approximate Multipliers

Original language description

There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural networks (CNN). The method allows approximate operations to be used in CNNs to reduce the power consumption of a target hardware implementation. During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size, and power consumption. The most suitable approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with common human-created CNNs of a similar complexity on the CIFAR-10 benchmark problem.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

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/GA21-13001S" target="_blank" >GA21-13001S: Automated design of hardware accelerators for resource-aware machine learning</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Article name in the collection

Genetic Programming, 24th European Conference, EuroGP 2021

ISBN

978-3-030-72812-0

ISSN

—

e-ISSN

—

Number of pages

16

Pages from-to

82-97

Publisher name

Springer Nature Switzerland AG

Place of publication

Seville

Event location

Seville

Event date

Apr 7, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000894232700006