Hardware asynchronous cellular automata of spiking neural networks on SoC for autonomous machines

The research field of artificial intelligence (AI) has long abode by the top-down problem solving strategy. Yet, we have adopted bottom-up design thinking to solve its hard problems. To tackle end-to-end AI-hard problems, a highly self-adaptive control system-on-chip (SoC) has been developed to self-learn its internal and external resources with the aid of sets of sensors and actuators. Inspired by biological cell learning theory, different approaches of modelling techniques have been derived together with machine learning (ML) methods to the embedded control systems so as to perform different tasks. This paper lays out our developments of the above.