| Title |
A Reconfigurable Spiking Neural Network Computing-in-memory Processor using 1T1C eDRAM for Enhanced System-level Efficiency |
| Authors |
(Sangmyoung Lee) ; (Seryeong Kim) ; (Soyeon Kim) ; (Soyeon Um) ; (Sangjin Kim) ; (Sanyeob Kim) ; (Wooyoung Jo) ; (Hoi-jun Yoo) |
| DOI |
https://doi.org/10.5573/JSTS.2025.25.4.355 |
| Keywords |
1T1C eDRAM; Computing-in-memory; network-on-chip; spiking neural network; system-level efficiency |
| Abstract |
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. |