Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

Abstract

A convolutional neural network (CNN) classifies images with high accuracy. However, CNN operation requires a large number of computations which consume a significant amount of power when implemented on hardware. Precision scaling has been recently used to reduce the hardware requirements and power consumption. In this paper, we present an energy-efficient precision-scaled CNN (EEPS-CNN) architecture. Furthermore, the Field Programmable Gate Array (FPGA) is reconfigured during run time using Dynamic Partial Reconfiguration (DPR). If the battery level decreases, the EEPS-CNN design with the most appropriate power consumption is configured on the FPGA. DPR enables recognition applications to run at a low power budget while sacrificing minor accuracy instead of termination. The proposed architecture is implemented on Xilinx XC7Z020 FPGA and is evaluated on three datasets: MNIST, F-MNIST, and SVHN datasets. The results show a 2.2X, 2.39X, and 2.38X reduction in the energy consumption, respectively, while using only 7 bits to represent all inputs and network parameters. The accuracy of the proposed EEPS-CNN is only 0.53%, 3.67%, and 0.88% less than 32-bit floating-point architectures for MNIST, F-MNIST, and SVHN, respectively. Moreover, the results show up to 92.91X and 4.84X reductions in the power and energy consumption of the proposed EEPS-CNN compared to related designs developed for the MNIST dataset.

Keywords

• Approximate computing
• convolutional neural network
• dynamic partial reconfiguration
• energy efficiency
• precision scaling