Robust analogue neuromorphic hardware networks using intrinsic physics-adaptive learning

Abstract Analogue neuromorphic computing hardware is highly energy-efficient and has been regarded as one of the most promising technologies for advancing artificial intelligence¹⁻⁷. However, the robust deployment of these analogue neuromorphic systems is hindered by stochastic variations of analogue devices and dynamic changes in hardware structure⁸, ⁹. Here, we demonstrate an approach called intrinsic physics-adaptive learning (IPAL) that can effectively train analogue neuromorphic networks based on non-ideal hardware. This approach allows us to obtain gradients of the practical physical system by using two-step physical operations (i.e., applying stimuli to the physical system and observing its resulting response), eliminating the need for mathematical modelling of the physical system. Experiments validate the effectiveness of IPAL on a neuromorphic hardware network comprised of analogue in-memory computing arrays and analogue activation units. Furthermore, we demonstrate that the approach can effectively train analogue neuromorphic networks with unpredictable hardware variations or impairments, and restore their recognition accuracy even when 60% of electrical synapses and neurons fail to work. We also show that IPAL can be used for analogue neuromorphic networks based on emerging memristive devices. Our work paves the way for developing analogue neuromorphic computing hardware with superior fault-tolerant performance.