Compute-in-memory implementation of state space models for event sequence processing

Spiking neural networks are generally used for sequential information and event data processing but still lack high performance. Through algorithm and hardware co-design, Zhang et al. report a State Space Model based approach to implement on compute-in memory hardware, enabling asynchronous and real-time processing capability with high energy efficiency for event sequences.