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 II: Model size(MB) and precisions
['[EMPTY]', 'Model Size(MB) Full-precision CNN', 'Model Size(MB) BNN', 'Precision(%) Full-precision CNN', 'Precision(%) BNN']
Accuracy and Model Size. First, we run three binarized benchmark networks using PhoneBit, and compare their accuracy and model size with full-precision networks. The compared results are shown in Tab. The model size of networks is critical for space-constrained mobile devices. For all three benchmark networks, PhoneBit can achieve significant compression rate with an acceptable accuracy loss. The model size of PhoneBit is on average 19.6× smaller than full-precision networks. For large network model such as VGG16, PhoneBit can achive 17.2× storage efficiency with 4.7% accuracy loss when compared to full-precision networks.