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In this paper, we propose an efficient predefined structured sparsity-basedex-situ training framework for a hybrid CMOS-memristive neuromorphic hardwarefor deep neural network to significantly lower the power consumption andcomputational complexity and improve scalability. The structure is verified ona wide range of datasets including MNIST handwritten recognition, breast cancerprediction, and mobile health monitoring. The results of this study show thatcompared to its fully connected version, the proposed structure providessignificant power reduction while maintaining high classification accuracy.

SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application