Cerebellum-inspired memtransistors enable emergent differentiation for hardware-efficient novelty detection

Artificial intelligence (AI) algorithms are currently executed using silicon-based hardware, resulting in excessively high energy demand for data centers. Edge computing AI for healthcare, robotics, and autonomous vehicles presents even stricter power and latency constraints, which are unmet by incumbent computing architectures. With vastly superior energy efficiency, biological neuronal networks provide hints towards alternative computational approaches including memory-logic colocation, asynchronous parallelism, and spike-triggered computation. Here, we draw inspiration from the cerebellum to demonstrate asymmetric-contact-gated MoS2 memtransistors that exhibit bias-polarity-dependent excitatory/inhibitory short-term plasticity. Arrays of these cerebellum-inspired memtransistors exploit the evolving interplay between excitatory and inhibitory responses to emulate the emergent synaptic differentiation of the cerebellum, enabling rapid identification of novel events. When applied to electrocardiogram data, arrhythmias are detected within a single heartbeat with 10,000-fold fewer operations than existing silicon-based approaches. In this manner, cerebellum-inspired neuromorphic hardware provides a pathway to computationally efficient, high-speed novelty detection for edge intelligence. The human brain corrects transient errors arising from discrepancies between actual objects and expectations, thereby maintaining smooth functional stability. Kang et al. report a MoS2-based device that realizes a bio-realistic approach to novelty detection, achieving higher efficiency than silicon-based electronics.