Topology optimization of random memristors for input-aware dynamic SNN

Machine learning has advanced unprecedentedly, exemplified by GPT- 4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, runtime re-configurability, and hardware architecture. To address these challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). PRIME uses spiking neurons to emulate brain’s spiking mechanisms and optimizes the topology of random memristive SNNs inspired by structural plasticity, ef-fectively mitigating memristor programming stochasticity. It also uses the input- aware early- stop policy to reduce latency and leverages memristive in- memory computing to mitigate von Neumann bottleneck. Validated on a 40- nm, 256- K memristor- based macro, PRIME achieves comparable classification accuracy and inception score to soft-ware baselines, with energy efficiency improvements of 37.8× and 62.5×. In addition, it reduces computational loads by 77 and 12.5% with minimal performance degradation and demonstrates robustness to stochastic mem-ristor noise. PRIME paves the way for brain-inspired neuromorphic computing.