  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.
