Evolutionary Neural Architecture Search Supporting Approximate Multipliers

Evolutionary Neural Architecture Search Supporting Approximate Multipliers

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There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural networks (CNN). The method allows approximate operations to be used in CNNs to reduce the power consumption of a target hardware implementation. During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size, and power consumption. The most suitable approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with common human-created CNNs of a similar complexity on the CIFAR-10 benchmark problem.

There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods utilizing multi-objective evolutionary algorithms are especially useful when the objective is not only to minimize the network error but also to minimize the number of parameters (weights) or power consumption of the inference phase. We propose a multi-objective NAS method based on Cartesian genetic programming for evolving convolutional neural networks (CNN). The method allows approximate operations to be used in CNNs to reduce the power consumption of a target hardware implementation. During the NAS process, a suitable CNN architecture is evolved together with approximate multipliers to deliver the best trade-offs between the accuracy, network size, and power consumption. The most suitable approximate multipliers are automatically selected from a library of approximate multipliers. Evolved CNNs are compared with common human-created CNNs of a similar complexity on the CIFAR-10 benchmark problem.

Approximate computing, Convolutional neural network, Cartesian genetic programming, Neuroevolution, Energy efficiency 

Approximate computing, Convolutional neural network, Cartesian genetic programming, Neuroevolution, Energy efficiency 

PIŇOS, M.; MRÁZEK, V.; SEKANINA, L.

2022

07.04.2021

Springer Nature Switzerland AG

Seville

978-3-030-72812-0

Genetic Programming, 24th European Conference, EuroGP 2021

Lecture Notes in Computer Science, vol 12691

12691

82

97

16

https://link.springer.com/chapter/10.1007%2F978-3-030-72812-0_6