Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138606" target="_blank" >RIV/00216305:26230/20:PU138606 - isvavai.cz</a>

Result on the web

<a href="https://arxiv.org/abs/2004.10483" target="_blank" >https://arxiv.org/abs/2004.10483</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/AICAS48895.2020.9073837" target="_blank" >10.1109/AICAS48895.2020.9073837</a>

Result language

angličtina

Original language name

Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks

Original language description

Approximate circuits have been developed to provide good tradeoffs between power consumption and quality of service in error resilient applications such as hardware accelerators of deep neural networks (DNN). In order to accelerate the approximate circuit design process and to support a fair benchmarking of circuit approximation methods, libraries of approximate circuits have been introduced. For example, EvoApprox8b contains hundreds of 8-bit approximate adders and multipliers. By means of genetic programming we generated an extended version of the library in which thousands of 8- to 128-bit approximate arithmetic circuits are included. These circuits form Pareto fronts with respect to several error metrics, power consumption and other circuit parameters. In our case study we show how a large set of approximate multipliers can be used to perform a resilience analysis of a hardware accelerator of ResNet DNN and to select the most suitable approximate multiplier for a given application. Results are reported for various instances of the ResNet DNN trained on CIFAR-10 benchmark problem. 

Czech name

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Czech description

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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/GA19-10137S" target="_blank" >GA19-10137S: Designing and exploiting libraries of approximate circuits</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Article name in the collection

2nd IEEE International Conference on Artificial Intelligence Circuits and Systems

ISBN

978-1-7281-4922-6

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

243-247

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Genoa

Event location

Genoa

Event date

Mar 23, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000720328700055