Analog neuromorphic computing enabled by multi-gate programmable resistive devices

Analog neural networks represent a massively parallel computing paradigm by mimicking the human brain. Two important functions that are not efficiently built by CMOS technology for their practical hardware implementations are weighting for synapse circuits and summing for neuron circuits. In this paper we propose the use of tunable analog resistances, such as multi-gate graphene devices, to efficiently enable these two functions. We design and demonstrate a complete analog neuromorphic circuitry enabled by such devices. Simulation results based on Verilog-A compact models for graphene devices confirm its functionality. We also provide experimental demonstration of our proposed graphene device along with projected circuit performance based on scaling targets. Our proposed design is suitable not only for the device example shown in this paper, but also for any beyond-CMOS technology that exhibits similar device characteristics.