  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.
