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MRÁZEK, V. SARWAR, S. SEKANINA, L. VAŠÍČEK, Z. ROY, K.
Original Title
Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Approximate computing, Neural networks, Logic synthesis, Low power, Genetic programming
Authors
MRÁZEK, V.; SARWAR, S.; SEKANINA, L.; VAŠÍČEK, Z.; ROY, K.
Released
7. 9. 2016
Publisher
Association for Computing Machinery
Location
Austin, TX
ISBN
978-1-4503-4466-1
Book
Proceedings of the IEEE/ACM International Conference on Computer-Aided Design
Pages from
811
Pages to
817
Pages count
7
URL
https://www.fit.vut.cz/research/publication/11142/
BibTex
@inproceedings{BUT133493, author="Vojtěch {Mrázek} and Syed Shakib {Sarwar} and Lukáš {Sekanina} and Zdeněk {Vašíček} and Kaushik {Roy}", title="Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks", booktitle="Proceedings of the IEEE/ACM International Conference on Computer-Aided Design", year="2016", pages="811--817", publisher="Association for Computing Machinery", address="Austin, TX", doi="10.1145/2966986.2967021", isbn="978-1-4503-4466-1", url="https://www.fit.vut.cz/research/publication/11142/" }