  Neuromorphic computing using post-CMOS technologies is gaining immense
popularity due to its promising abilities to address the memory and power
bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC
- a reconfigurable and energy efficient architecture built-on Memristive
Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were
primarily focused on device and circuit implementations of SNNs on crossbars.
RESPARC advances this by proposing a complete system for SNN acceleration and
its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for
inner-product computation and realizes a hierarchical reconfigurable design to
incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate
the proposed architecture on different SNNs ranging in complexity from 2k-230k
neurons and 1.2M-5.5M synapses. Simulation results on these networks show that
compared to the baseline digital CMOS architecture, RESPARC achieves 500X (15X)
efficiency in energy benefits at 300X (60X) higher throughput for multi-layer
perceptrons (deep convolutional networks). Furthermore, RESPARC is a
technology-aware architecture that maps a given SNN topology to the most
optimized MCA size for the given crossbar technology.
