Design considerations of synaptic device for neuromorphic computing

Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital Boolean computing. Recently, two-terminal emerging memory devices that show electrically-triggered resistance modulation have been proposed as synaptic devices for neuromorphic computing. The synaptic device candidates include phase change memory (PCM), resistive RAM (RRAM) and conductive bridge RAM (CBRAM), etc. In this paper, we discuss the general design considerations of synaptic devices for plasticity and learning. As a rule of thumb for performance metrics assessment, an ideal synaptic device should have characteristics such as dimension, energy consumption, operation frequency, dynamic range, etc. that are scalable to biological systems with comparable complexity.