Publication Details
Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
approximate circuit, genetic programming, convolutional neural network, hardware
accelerator, optimization
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.
@article{BUT168178,
author="Vojtěch {Mrázek} and Lukáš {Sekanina} and Zdeněk {Vašíček}",
title="Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators",
journal="IEEE Journal on Emerging and Selected Topics in Circuits and Systems",
year="2020",
volume="10",
number="4",
pages="406--418",
doi="10.1109/JETCAS.2020.3032495",
issn="2156-3357",
url="https://www.fit.vut.cz/research/publication/12372/"
}