A Training-Efficient Hybrid-Structured Deep Neural Network With Reconfigurable Memristive Synapses

The continued success in the development of neuromorphic computing has immensely pushed today’s artificial intelligence forward. Deep neural networks (DNNs), a brainlike machine learning architecture, rely on the intensive vector–matrix computation with extraordinary performance in data-extensive applications. Recently, the nonvolatile memory (NVM) crossbar array uniquely has unvailed its intrinsic vector–matrix computation with parallel computing capability in neural network designs. In this article, we design and fabricate a hybrid-structured DNN (hybrid-DNN), combining both depth-in-space (spatial) and depth-in-time (temporal) deep learning characteristics. Our hybrid-DNN employs memristive synapses working in a hierarchical information processing fashion and delay-based spiking neural network (SNN) modules as the readout layer. Our fabricated prototype in 130-nm CMOS technology along with experimental results demonstrates its high computing parallelism and energy efficiency with low hardware implementation cost, making the designed system a candidate for low-power embedded applications. From chaotic time-series forecasting benchmarks, our hybrid-DNN exhibits $1.16\times $ – $13.77\times $ reduction on the prediction error compared to the state-of-the-art DNN designs. Moreover, our hybrid-DNN records 99.03% and 99.63% testing accuracy on the handwritten digit classification and the spoken digit recognition tasks, respectively.