Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search

O. Krestinskaya, K. Salama, A. P. James
2020 IEEE International Symposium on Circuits and Systems (ISCAS), (2020)


Memristors, Hardware, Aging, Neurons, System-on-chip, Power demand, Optimization


​The selection of hyperparameters and circuit components for optimum hardware implementation of a neural network is a challenging task, which has not been automated yet. This work proposes the method for the selection of optimum neural network architecture and hyperparameters using genetic algorithm based on the hardware-related performance metrics, such an on-chip area, power consumption, processing time and robustness to hardware non-idealities, and focus on memristor-based analog network architecture. The experimental results show that the proposed approach allows to select the optimum architecture based on the designers' preferences.


DOI: 10.1109/ISCAS45731.2020.9180514


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