PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile PhonesThis paper has been submitted to Design, Automation and Test in Europe(DATE) on Sept-15th-2019, and accepted on Nov-7th-2019. Email: cheng83@mail.sysu.edu.cn
Table IV: Energy consumption per image frame for YOLOv2 Tiny network on Snapdragon 820 platform
['[EMPTY]', 'CNNdroid CPU', 'CNNdroid GPU', 'Tensorflow Lite CPU', 'Tensorflow Lite GPU', 'Tensorflow Lite CPU Quant', 'PhoneBit']
Energy Efficiency. Energy efficiency is a major design concern for energy-restricted mobile devices. Now, we demonstrate the energy efficiency of PhoneBit by reporting power consumption and energy efficiency under different frameworks. Tab. From Tab. PhoneBit consumes around 226mW power and obtains 105.26 FPS per watt. Compared with CNNdroid and TFLite implementations, PhoneBit brings 4×–1218× faster execution speed, 2×– 4× lower power dissipation and 24×–5263× better performance per power efficiency.