Open AccessEvolutionary Learning of Binary Neural Network Using a TaO x Memristor via Stochastic Stateful Logic
Author(s) -
Kim Do Hoon,
Kim Young Seok,
Cheong Woon Hyung,
Song Hanchan,
Rhee Hakseung,
Kay Sooyeon Narie,
Han Jin-Woo,
Kim Kyung Min
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200058
Subject(s) - stateful firewall , neuromorphic engineering , memristor , computer science , artificial neural network , genetic algorithm , crossover , artificial intelligence , theoretical computer science , computer architecture , machine learning , electronic engineering , engineering , computer network , network packet
Memristive stateful logic for Boolean computers and memristive neural networks for neuromorphic computers are two distinct emerging applications enabled by memristors in future computing. Interestingly, they both utilize an identical crossbar array platform, suggesting their simultaneous implementation is possible. Herein, a new methodology combining the two technologies to create synergy in neuromorphic computing is proposed. A genetic algorithm in the memristive neural network is introduced, where the stochastic stateful logic realizes the required mutation and crossover operators. Under optimized genetic evolution conditions with the fittest selection algorithm, without any backpropagation circuits, the modified national institute of standards and technology dataset recognition accuracy of 90.1% for a 784 × 100 size network is anticipated, which is comparable to 93.9% accuracy using a conventional deep neural network.