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

O. Krestinskaya, Khaled N. Salama, A. P. James

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publication2020 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
ISBN (Print)978-1-7281-3321-8
DOIs
StatePublished - 2020

Fingerprint Dive into the research topics of 'Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search'. Together they form a unique fingerprint.

Cite this