Energy‐efficient computing for machine learning based target detection

Abstract

SummaryThe main objective of this study is to develop real‐time, energy‐efficient embedded computing for machine learning based target detection. Convolutional neural network (CNN) model based detection, a machine learning technique, can provide higher detection accuracy than constant false alarm rate (CFAR) detection techniques even if it results in higher processing costs. In this study, we achieve three significant improvements for real‐time radar target detection by considering computational cost. The first improvement is to reduce the computational cost of the CNN model. The second achievement is the design of heterogeneous computing optimizations. The third of them is to support energy‐efficient computing solutions for mobile sensors. Compared to the initial CNN model, layer improvements decreased the number of operations by 8.5x. Real‐time operations are satisfied by hardware‐specific improvements like vectorization and parallelization. The embedded NVIDIA Jetson GPU and Intel MYRIAD VPU, which have power consumption of 15 Watts and 1 Watt, respectively, have been used to execute the energy‐efficient target detection. The most energy‐efficient solution is achieved by using Jetson AGX Xavier GPU with 32‐bit single precision and 15 Watts of power consumption.