A Reconfigurable Spiking Neural Network Computing-in-memory Processor using 1T1C eDRAM for Enhanced System-level Efficiency (유회준 교수 연구실)

Spiking Neural Network (SNN) Computing-In-Memory (CIM) achieves high macro-level energy efficiency but struggles with system-level efficiency due to excessive external memory access (EMA) caused by intermediate activation memory demands. To address this, a high-capacity SNN-CIM capable of managing large weight loads is essential. This paper introduces a high-density 1T1C eDRAM-based SNN-CIM processor that significantly enhances system-level energy efficiency through two key features: a high-density, low-power Reconfigurable Neuro-Cell Array (ReNCA) that reuses the 1T1C cell array and employs a charge pump, achieving a 41% area and 90% power reduction and a reconfigurable CIM architecture with dual-mode ReNCA and Dynamic Adjustable Neuron Link (DAN Link) to optimize EMA for activations and weights. These innovations collectively improve system-level energy efficiency by 10×, setting a new benchmark for performance.