Circuit Techniques for Online Learning of Memristive Synapses in CMOS-Memristor Neuromorphic Systems.

Memristors are widely leveraged in neuromorphic systems for constructing synapses. Resistance switching characteristics of memristors enable online learning in synapses. This paper addresses a fundamental issue associated with the design of synapses with memristors whose switching rates in either direction differ up to two orders of magnitude. A twin-memristor synapse that uses memristors with identical switching rates is first presented. It is shown this design fails in the case of disproportionate switching times. To circumvent this issue, a quad-memristor synapse is also considered. The scheme used for online learning of the synapse circuit implementation, and simulation results are also presented. To compare the two synapses, their area, clock frequency, dynamic power and energy per spike values are provided.