Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138669" target="_blank" >RIV/00216305:26230/20:PU138669 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/publication/12372/" target="_blank" >https://www.fit.vut.cz/research/publication/12372/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JETCAS.2020.3032495" target="_blank" >10.1109/JETCAS.2020.3032495</a>
Alternative languages
Result language
angličtina
Original language name
Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators
Original language description
Libraries of approximate circuits are composed of fully characterized digital circuits that can be used as building blocks of energy-efficient implementations of hardware accelerators. They can be employed not only to speed up the accelerator development but also to analyze how an accelerator responds to introducing various approximate operations. In this paper, we present a methodology that automatically builds comprehensive libraries of approximate circuits with desired properties. Target approximate circuits are generated using Cartesian genetic programming. In addition to extending the EvoApprox8b library that contains common approximate arithmetic circuits, we show how to generate more specific approximate circuits; in particular, MxN-bit approximate multipliers that exhibit promising results when deployed in convolutional neural networks. By means of the evolved approximate multipliers, we perform a detailed error resilience analysis of five different ResNet networks. We identify the convolutional layers that are good candidates for adopting the approximate multipliers and suggest particular approximate multipliers whose application can lead to the best trade-offs between the classification accuracy and energy requirements. Experiments are reported for CIFAR-10 and CIFAR-100 data sets.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
Result continuities
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)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
ISSN
2156-3357
e-ISSN
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Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
406-418
UT code for WoS article
000598110700002
EID of the result in the Scopus database
2-s2.0-85093670630