HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system.

Abstract

Computers based on the Turing model execute artificial intelligence (AI) algorithms that are either programmed by humans or derived from machine learning. These AI algorithms cannot be modified during the operation process according to environmental changes, resulting in significantly poorer adaptability to new environments, longer learning latency, and higher power consumption compared to the human brain. In contrast, neurobiological circuits can function while simultaneously adapting to changing conditions. Here, we present a brain-inspired Super-Turing AI model based on a synaptic resistor circuit, capable of concurrent real-time inference and learning. Without any prior learning, a circuit of synaptic resistors integrating ferroelectric HfZrO materials was demonstrated to navigate a drone toward a target position while avoiding obstacles in a simulated environment, exhibiting significantly superior learning speed, performance, power consumption, and adaptability compared to computer-based artificial neural networks. Synaptic resistor circuits enable efficient and adaptive Super-Turing AI systems in uncertain and dynamic real-world environments.