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VAŠÍČEK, Z. MRÁZEK, V. SEKANINA, L.
Original Title
Automated Circuit Approximation Method Driven by Data Distribution
Type
conference paper
Language
English
Original Abstract
We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best trade-offs between the classification accuracy and power consumption of two image classifiers based on neural networks.
Keywords
digital circuit, approximate circuit, functional approximation, neural network
Authors
VAŠÍČEK, Z.; MRÁZEK, V.; SEKANINA, L.
Released
26. 3. 2019
Publisher
European Design and Automation Association
Location
Florence
ISBN
978-3-9819263-2-3
Book
Design, Automation and Test in Europe Conference
Pages from
96
Pages to
101
Pages count
6
URL
https://www.fit.vut.cz/research/publication/11821/
BibTex
@inproceedings{BUT156843, author="Zdeněk {Vašíček} and Vojtěch {Mrázek} and Lukáš {Sekanina}", title="Automated Circuit Approximation Method Driven by Data Distribution", booktitle="Design, Automation and Test in Europe Conference", year="2019", pages="96--101", publisher="European Design and Automation Association", address="Florence", doi="10.23919/DATE.2019.8714977", isbn="978-3-9819263-2-3", url="https://www.fit.vut.cz/research/publication/11821/" }