2021/08/30 by Xinjie Zhang, Zhang, Xinjie, Jiawei Shao +5
Computer Science · Engineering · Mathematics · #Advanced Neural Network Applications #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Computation #Computer science #Edge device #Encoder #Engineering #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Feature vector #Inference #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Machine learning #Mobile device #Mobile edge computing #Overhead (engineering) #Parallel computing #Process (computing) #Signal Processing (eess.SP) #Speedup #Task (project management) #cs.AI #cs.LG #eess.SP #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2108.13009
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/08/30 · arxiv created 2021/08/31 · arxiv updated 2021/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications. To accelerate the inference process, on-device model sparsification and intermediate feature compression are regarded as two prominent techniques. However, as the on-device model sparsity level and intermediate feature compression ratio have direct impacts on computation workload and communication overhead respectively, and both of them affect the inference accuracy, finding the optimal values of these hyper-parameters brings a major challenge due to the large search space. In this paper, we endeavor to develop an efficient algorithm to determine these hyper-parameters. By selecting a suitable model split point and a pair of encoder/decoder for the intermediate feature vector, this problem is casted as a sequential decision problem, for which, a novel automated machine learning (AutoML) framework is proposed based on deep reinforcement learning (DRL). Experiment results on an image classification task demonstrate the effectiveness of the proposed framework in achieving a better communication-computation trade-off and significant inference speedup against various baseline schemes.