Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F16%3APU122828" target="_blank" >RIV/00216305:26230/16:PU122828 - isvavai.cz</a>

Result on the web

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11142" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11142</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2966986.2967021" target="_blank" >10.1145/2966986.2967021</a>

Result language

angličtina

Original language name

Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks

Original language description

Artificial neural networks (NN) have shown a significant promise in difficult tasks like image classification or speech recognition. Even well-optimized hardware implementations of digital NNs show significant power consumption. It is mainly due to non-uniform pipeline structures and inherent redundancy of numerous arithmetic operations that have to be performed to produce each single output vector.  This paper provides a methodology for the design of well-optimized power-efficient NNs with a uniform structure suitable for hardware implementation. An error resilience analysis was performed in order to determine key constraints for the design of approximate multipliers that are employed in the resulting structure of NN. By means of a search based approximation method, approximate multipliers showing desired tradeoffs between the accuracy and implementation cost were created. Resulting approximate NNs, containing the approximate multipliers, were evaluated using standard benchmarks (MNIST dataset) and a real-world classification problem of Street-View House Numbers. Significant improvement in power efficiency was obtained in both cases with respect to regular NNs. In some cases, 91% power reduction of multiplication led to classification accuracy degradation of less than 2.80%. Moreover, the paper showed the capability of the back propagation learning algorithm to adapt with NNs containing the approximate multipliers. 

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

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2016

Confidentiality

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

Article name in the collection

Proceedings of the IEEE/ACM International Conference on Computer-Aided Design

ISBN

978-1-4503-4466-1

ISSN

—

e-ISSN

—

Number of pages

7

Pages from-to

811-817

Publisher name

Association for Computing Machinery

Place of publication

Austin, TX

Event location

Austin, TX

Event date

Nov 7, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000390297800081