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 I: Mobile devices
['Device', 'SOC', 'Memory', 'OS', 'OpenCL Version', 'ALUs in GPU']
Experiment Setup. PhoneBit is evaluated on two mobile devices with different SoCs: Snapdragon 820 and Snapdragon 855. The detailed hardware configurations are shown in Tab. We evaluate PhoneBit with three classic neural network models, namely, AlexNet network for CIFAR10 dataset, YOLOv2 Tiny network for VOC2007 dataset and VGG16 network for CIFAR10 dataset. We conduct the experiments to evaluate the efficiency of PhoneBit by the extensive comparison with state-of-the-art frameworks: Both CPU- and GPU-based executions are evaluated for performance comparison. TensorFlow Lite (TFLite) TFLite supports accelerators on both CPUs and GPUs. Due to these limitations, three implementations, including one GPU-based and two CPU-based executions with and without quantization, are evaluated for performance comparison. We implement forwarding of the benchmark networks on the mobile devices. The metrics of accuracy, model size, runtime, power and energy consumption are evaluated and compared to demonstrate the efficiency of PhoneBit.