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 III: Average runtime(ms) in Snapdragon 820 and Snapdragon 855 platforms using different frameworks
['[EMPTY]', 'Snapdragon 820 CNNdroid', 'Snapdragon 820 CNNdroid', 'Snapdragon 820 Tensorflow Lite', 'Snapdragon 820 Tensorflow Lite', 'Snapdragon 820 Tensorflow Lite', 'Snapdragon 820 PhoneBit', 'Snapdragon 855 CNNdroid', 'Snapdragon 855 CNNdroid', 'Snapdragon 855 Tensorflow Lite', 'Snapdragon 855 Tensorflow Lite', 'Snapdragon 855 Tensorflow Lite', 'Snapdragon 855 PhoneBit']
Runtime Performance. Next, we will demonstrate the efficiency of PhoneBit by comparing runtime performance with state-of-the-art frameworks. Tab. From Tab. (1) PhoneBit brings significant speedups over all of the compared frameworks on both mobile devices. 794× and 35× speedups on CPU based and GPU based executions, respectively. On TFLite, PhoneBit can on average bring 12×, 15×, and 6× faster speedup over CPU based, GPU based and CPU quantization based executions, respectively. (2) Stability issues have been observed on CNNdroid and TFLite frameworks. For large-scale network VGG16, CNNdroid runs out of memory (OOM) on both CPU and GPU implementations while TFLite runs into crash (CRASH) and fails to produce the results on GPU based executions. In contrast, PhoneBit can work flawlessly with all benchmark networks with faster inference speed.