Evaluation of Neuromorphic Hardware using Cellular Neural Networks and Oxide Semiconductors

As the performance of integrated circuits has improved, attention has focused on functions of the brain having the most advanced information processing capability. Therefore, attempts to realize the functions with engineering approach have been actively conducted around the world. Current mainstream of digital computers have strict, logical, serial, and concentrated information processing functions, but they do not have flexible, intuitive, parallel, and distributed functions like the human brain. Therefore, it is expected that neuromorphic computing has unprecedented innovative processing functions. The time required for information processing of brain-type computing is determined by the response time of the memory. Therefore, conventional digital computers require several tens of steps for the associated memory operations, but analog neuromorphic systems can do so in one step. In this article, we have implemented and evaluated neuromorphic hardware modeling human brain neurons using cellular neural networks and oxide semiconductor a-IGZO. A cellular neural network is used because it is suitable for large-scale integration of electronic equipment. Since oxide semiconductor elements can be used as synaptic elements by improved Hebb learning, deterioration in characteristics is thereof used as strength plasticity of synaptic connection. A letter correction system is developed by a cellular neural network and oxide semiconductor synapses, and the letter correction rate is evaluated by logic simulation.