Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip

Neuromorphic computing, inspired by brain neurons and synapses and implemented via spiking neural networks (SNNs) on neuromorphic chips, promises energy-efficient machine intelligence. A central challenge is leveraging high-level brain dynamic mechanisms to realize energy advantages. This work presents an application-oriented algorithm–software–hardware co-designed neuromorphic system. We design and fabricate an asynchronous sensing–computing system-on-chip (SoC) named Speck, featuring a processor resting power of 0.42 mW and always-on behavior where no input consumes no running energy. We identify a common but overlooked “dynamic imbalance” in SNNs—similar firing responses across diverse inputs—and develop an attention-based dynamic framework to enable input-dependent energy variance. Together, the system demonstrates real-time operation with total power as low as 0.70 mW and ultra-low latency. Results highlight the potential of asynchronous, event-driven, sparse, and dynamic neuromorphic computing.