边端融合的终端情境自适应深度感知模型

The end context adaptative of deep models with edge-end collaboration was analyzed. The partition and alternating direction method of multiplier method (X-ADMM) was proposed. The model compression was employed to simplify the model structure, and the model was partitioned at layer granularity to find the best partition point. The model can collaborate with edge-end devices to improve model operation efficiency. The graph based adaptive DNN surgery algorithm (GADS) was proposed in order to realize the dynamic adaptation of model partition. The model will preferentially search for the partition point that best meets resource constraints among surrounding partition states to achieve rapid adaptation when the running context (e.g., storage, power, bandwidth) of the model changes. The experimental results showed that the model realized the adaptive tuning of model partition point in an average of 0.1 ms. The total running latency was reduced by 56.65% at the highest with no more than 2.5% accuracy loss.