Intra- and Inter-Layer Scheduling Exploration and Optimization for ReRAM-Based DNN Accelerators

Resistive Random Access Memory (ReRAM) based architectures have shown great potential for realizing energy-efficient Deep Neural Network (DNN) acceleration. When deploying a DNN, the ReRAM-based designs need a scheduling scheme to translate massive hardware resources into actual performance. Different scheduling schemes would lead to different levels of data reuse and computational parallelism, resulting in different energy efficiency and performance. However, the ReRAM-based scheduling scheme faces the following limitations. First, current studies mainly focus on intra-layer scheduling scheme optimizations by using the Weight Stationary (WS) data flow. These studies ignore the difference between layers and limit optimization opportunities. Second, there is no systematic definition and analysis for inter-layer scheduling schemes. Third, there is no co-optimization study on intra- and inter-layer scheduling schemes. Fourth, the complex network structure leads to intricate inter-layer data dependency, making the optimization of the scheduling scheme more challenging. These limitations restrict the comprehensive understanding of the scheduling schemes. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inspired by these observations, we identify the fundamental impact of intra-layer scheduling schemes on ReRAM-based designs, including the WS and Input Stationary (IS) data flows. We also systematically define and analyze inter-layer scheduling schemes according to different combinations of data flows, including the WS-WS, IS-IS, WS-IS, and IS-WS data flows. We analyze and explore different resource allocation strategies for these schemes. We also propose the intra- and inter-layer co-optimization to further improve performance and energy efficiency. Then, we propose a method for building a hybrid scheduling scheme by flexibly combining these inter-layer scheduling schemes for complex networks. Finally, we seek the potential to improve performance and energy efficiency for hybrid scheduling schemes. For deploying the MobileNet-V1, ResNet-18, VGG-16, and AlexNet, the hybrid scheduling scheme improves performance by 10.2×~130.7×, 1.9×~16.5×, 7.0×~56×, and 1×~153.1× than the WS-WS, IS-IS, IS-WS, and WS-IS based scheduling schemes, respectively. Similarly, the power efficiency can also be increased by 15× and 26× than the WS-WS and IS-IS based scheduling schemes, respectively.