边端融合的终端情境自适应深度感知模型

王虹力,郭斌,刘思聪,刘佳琪,仵允港,於志文

End context-adaptative deep sensing model with edge-end collaboration

Hong-li WANG,Bin GUO,Si-cong LIU,Jia-qi LIU,Yun-gang WU,Zhi-wen YU

表 1 不同方法下典型深度学习模型的推断时延和精度

Tab.1 Inference latency and accuracy of typical deep learning models with different approaches

网络 A0 /% T0 /ms γ TRAP-ADMM[19] /ms ARAP-ADMM /% TX-ADMM /ms AX-ADMM /% Alexnet 85.82 46.8 14.7 38.78 84.21 36.3 84.17 GoogleNet 87.48 943.6 15.6 883.95 84.91 532.6 84.60 Resnet-18 91.60 285.5 15.4 267.70 90.01 213.5 89.80 VGG-16 91.66 203.7 16.0 186.90 89.59 88.3 89.20 MobileNet 89.60 219.2 2.0 207.89 87.96 179.2 87.90 MobileNet 89.60 219.2 4.0 196.69 80.60 160.3 80.83 ShuffleNet 88.14 202.8 4.0 183.79 84.25 153.4 84.19