NBSSN: A Neuromorphic Binary Single-Spike Neural Network for Efficient Edge Intelligence.

Neuromorphic computing approaches such as Spiking Neural Networks (SNN) have been increasingly adopted in bio-signal processing and interpretation due to its intrinsic neurodynamic attribute. Nevertheless, reconciling performance and power efficiency in SNN implementation is still a bottleneck. Single-spike neural coding scheme, which is an extremely sparse coding scheme, provides a solution to bridge the gap. In this work, a neuromorphic architecture, using binary single spike neural signals, is proposed with both algorithm and hardware implementation. A sparsity-aware spatial-temporal backpropagation training method is proposed together with a singlespike coding scheme. Also, a novel neuromorphic accelerator is co-designed with algorithmic optimization and implemented in 40nm CMOS process. Experimental results show that the proposed processor reaches an accuracy of 94.61% on the MNIST dataset, 93.59% on the N-MNIST dataset, and 93.27% on the ECG dataset, respectively, while consumes 0.173 mu J per ECG classification task and 0.16mm(2) on-chip area. The overall power consumption is reduced by 91.68% compared to the state-of-the-art systems.