Feasibility Analysis and Implementation of Adaptive Dynamic Reconfiguration of CNN Accelerators

Abstract

In multi-tasking scenarios with dynamically changing loads, the parallel computing of convolutional neural networks (CNNs) causes high energy and resource consumption in the system. Another critical problem is that previous neural network hardware accelerators are often limited to fixed scenarios and lack the function of adaptive adjustment. To solve these problems, a reconfiguration adaptive system based on the prediction of algorithm workload is proposed in this paper. Deep Learning Processor Unit (DPU) from Xilinx has excellent performance in accelerating network computing. After summarizing the characteristics of hardware accelerators and gaining an in-depth understanding of the DPU structure, we propose a regression model for CNNs runtime prediction and a guidance scheme for adaptive reconfiguration combined with the characteristics of Deep Learning Processor Unit. For different DPU sizes, the accuracy of the proposed prediction model achieves 90.7%. With the dynamic reconfiguration technology, the proposed strategy can enable accurate and fast reconfiguration. In the load change scenario, the proposed system can significantly reduce power consumption.