System-level benchmark of synaptic device characteristics for neuro-inspired computing

Synaptic devices based on emerging non-volatile memory devices have been proposed to emulate analog synapses for neuro-inspired computing. However, the non-ideal device characteristics such as nonlinear and asymmetric weight increase/decrease, and finite on/off ratio, may adversely affect the learning accuracy at the system-level. In this paper, we present a device-circuit-algorithm co-simulation framework, i.e. NeuroSim, to systematically the metrics such as accuracy, area, latency and energy for online learning with synaptic devices. We surveyed a few representative synaptic devices in literature, and concluded that today's realistic devices are difficult to achieve accurate and fast learning. Finally, the targeted and ideal specifications for synaptic device engineering are proposed.