  There is unprecedented development in machine learning, exemplified by recent
large language models and world simulators, which are artificial neural
networks running on digital computers. However, they still cannot parallel
human brains in terms of energy efficiency and the streamlined adaptability to
inputs of different difficulties, due to differences in signal representation,
optimization, run-time reconfigurability, and hardware architecture. To address
these fundamental challenges, we introduce pruning optimization for input-aware
dynamic memristive spiking neural network (PRIME). Signal representation-wise,
PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent
spiking mechanism. Drawing inspiration from the brain's structural plasticity,
PRIME optimizes the topology of a random memristive spiking neural network
without expensive memristor conductance fine-tuning. For runtime
reconfigurability, inspired by the brain's dynamic adjustment of computational
depth, PRIME employs an input-aware dynamic early stop policy to minimize
latency during inference, thereby boosting energy efficiency without
compromising performance. Architecture-wise, PRIME leverages memristive
in-memory computing, mirroring the brain and mitigating the von Neumann
bottleneck. We validated our system using a 40 nm 256 Kb memristor-based
in-memory computing macro on neuromorphic image classification and image
inpainting. Our results demonstrate the classification accuracy and Inception
Score are comparable to the software baseline, while achieving maximal
62.50-fold improvements in energy efficiency, and maximal 77.0% computational
load savings. The system also exhibits robustness against stochastic synaptic
noise of analogue memristors. Our software-hardware co-designed model paves the
way to future brain-inspired neuromorphic computing with brain-like energy
efficiency and adaptivity.
