Spiking-based matrix computation by leveraging memristor crossbar array

As process technology continues scaling down, the memory barrier becomes more severe. Thus, spiking neuromorphic computing that can significantly enhance computing and communication efficiencies has been widely studied. Both conventional CMOS technology and emerging devices have been used in hardware implementation of spiking neuromorphic computing. Particularly, the memristor technology that can naturally emulate plasticity and energy efficiency of biological synapses have gained a lot of attention. However, the use of memristors in high density computation, such as matrix-vector operation, is still missing. In this work, a spiking (pulse-based) computing component that leverages memristor crossbar array is proposed for matrix-vector operation. We adopt the rate coding model and count the produced spike number during a given time period of T as the computation output. We carefully design the crossbar array structure and the integrate-and-fire circuit. The linear relationship between output spike numbers and the sum-of-production of input vector and matrix entries is observed in our simulation results. The proposed spiking computing design realizes matrix computation successfully and demonstrates good adaptability in neural network.