Memristive DeepLab: A hardware friendly deep CNN for semantic segmentation

Abstract

DeepLab—one of the most critical deep neural network models for image segmentation—has achieved the most advanced performance in the field of image semantic understanding. However, the rich parameters and calculation of deep convolutional neural networks (DCNNs) demand further research on neuro-inspired computing chips specifically designed for hardware acceleration and AI applications. This motivates memristive solutions—the memristor integrates a range of merits, such as fast speed and nanoscale device, to provide an ideal implementation for building low-power neural computing chips and ultra-high-density non-volatile memory. Here, this paper proposes a memristive DeepLab (MDeepLab) system with software-hardware co-design, in which atrous convolution is used to enlarge the receptive field of neurons, the measure to avoid the gridding effect is adopted. Also, the weights are stored by leveraging the unit of a single crossbar array and the constant-term circuit, which significantly reduces the number of memristor devices, doubles the density, and nearly halves the power consumption conventional crossbar array. Finally, the effectiveness of the proposed scheme is verified by a series of experimental simulations and result analysis, showing the superiority of MDeepLab. This study is expected to provide a new solution for low-power consumption and real-time image processing of edge devices.