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Neuromorphic computing using post-CMOS technologies is gaining immensepopularity due to its promising abilities to address the memory and powerbottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC- a reconfigurable and energy efficient architecture built-on MemristiveCrossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works wereprimarily focused on device and circuit implementations of SNNs on crossbars.RESPARC advances this by proposing a complete system for SNN acceleration andits subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs forinner-product computation and realizes a hierarchical reconfigurable design toincorporate the data-flow patterns in an SNN in a scalable fashion. We evaluatethe proposed architecture on different SNNs ranging in complexity from 2k-230kneurons and 1.2M-5.5M synapses. Simulation results on these networks show thatcompared to the baseline digital CMOS architecture, RESPARC achieves 500X (15X)efficiency in energy benefits at 300X (60X) higher throughput for multi-layerperceptrons (deep convolutional networks). Furthermore, RESPARC is atechnology-aware architecture that maps a given SNN topology to the mostoptimized MCA size for the given crossbar technology.

RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks