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

Abstract
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.

Citation
Krestinskaya, O., Salama, K., & James, A. P. (2020). Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search. 2020 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas45731.2020.9180514

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Conference/Event Name
2020 IEEE International Symposium on Circuits and Systems (ISCAS)

DOI
10.1109/ISCAS45731.2020.9180514

Additional Links
https://ieeexplore.ieee.org/document/9180514/https://ieeexplore.ieee.org/document/9180514/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9180514

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