Neural Synaptic Plasticity-Inspired Computing: A High Computing Efficient Deep Convolutional Neural Network Accelerator

Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in classification, natural language processing (NLP), and regression tasks. However, there is still a great gap between DCNNs and the human brain in terms of computation efficiency. Inspired by neural synaptic plasticity and stochastic computing (SC), we propose neural synaptic plasticity-inspired computing (NSPC) to simulate the human brain's neural network activity for inference tasks with simple logic gates. The multiplication and accumulation (MAC) is transformed by the wire connectivity in NSPC, which only requires bundles of wires and small width adders. To this end, the NSPC imitates the structure of neural synaptic plasticity from a circuit wires connection perspective. Furthermore, from the principle of NSPC, we use a data mapping method to convert the convolution operations to matrix multiplications. Based on the methodology of NSPC, fully-pipelined and low latency architecture is designed. The proposed NSPC accelerator exhibits high hardware efficiency while maintaining a comparable network accuracy level. The NSPC based DCNN accelerator (NSPC-CNN) processes DCNN at 1.5625M images/s with a power dissipation of 15.42 W and an area of 36.4 mm(2). The NSPC based deep neural network (DNN) accelerator (NSPC-DNN) that implements three fully connected layers DNN consumes only 6.6 mm(2) area and 2.93 W power, and achieves a throughput of 400M images/s. Compared with conventional fixed-point implementations, the NSPC-CNN achieves 2.77x area efficiency, 2.25x power efficiency; the proposed NSPC-DNN exhibits 2.31x area efficiency and 2.09x power efficiency.