Tutorial 1 — ReRAM-based analog synapse devices for neuromorphic system

摘要

To overcome the intrinsic limitations of von Neumann computing system with information bottleneck between memory and CPU, we need to develop neuromorphic computing system based on hardware artificial neural network (ANN). The ANN system with high density synapse devices can perform massive parallel computing for pattern recognition with low power consumption. To implement neuromorphic system with on-chip learning capability, we need to develop ideal synapse device with various device requirements such as scalability, multi-level cell (MLC) characteristics, low power operation, data retention, and symmetric and linear conductance change under potentiation/depression modes. Although various devices such as ReRAM, PRAM, and MRAM were proposed for synapse applications, these devices have intrinsic limitations for neuromorphic synapse application. This talk covers various ReRAM synapse devices such as filamentary switching ReRAM (HfO