Publication Details
Automated Circuit Approximation Method Driven by Data Distribution
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
digital circuit, approximate circuit, functional approximation, neural network
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.
@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/"
}