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

Abstract

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 $2.77 \times $ area efficiency, $2.25 \times $ power efficiency; the proposed NSPC-DNN exhibits $2.31 \times $ area efficiency and $2.09 \times $ power efficiency.