Neuromorphic Computing With Hybrid Memristive/Cmos Synapses For Real-Time Learning

Resistive (or memristive) devices, including resistive switching memory (RRAM), phase change memory (PCM) an spin-transfer torque memory (STTRAM), are strong candidates for future high-density memory, embedded memory and storage class memory. The availability of resistive-device technology in the industry would pave the way for several other applications in advanced computing, such as neuromorphic cognitive systems and other non-von Neumann approaches to computing. However, the building-block design, functionality and power consumption need to be carefully evaluated to assess all the advantages of the resistive devices with respect to standard CMOS technology. This work will review the recent progress in developing hybrid memristive/CMOS synapses based on either RRAM or PCM, showing the circuit design, the operation concept and the demonstration of real-time spike-based learning and recognition of visual patterns. The learning accuracy and power consumption of the novel synapse blocks will be finally discussed.