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Graphics processing units (GPUs) can improve deep neural network inferencethroughput via batch processing, where multiple tasks are concurrentlyprocessed. We focus on novel scenarios that the energy-constrained mobiledevices offload inference tasks to an edge server with GPU. The inference taskis partitioned into sub-tasks for a finer granularity of offloading andscheduling, and the user energy consumption minimization problem underinference latency constraints is investigated. To deal with the coupledoffloading and scheduling introduced by concurrent batch processing, we firstconsider an offline problem with a constant edge inference latency and the samelatency constraint. It is proven that optimizing the offloading policy of eachuser independently and aggregating all the same sub-tasks in one batch isoptimal, and thus the independent partitioning and same sub-task aggregating(IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm isproposed to optimally group tasks when the latency constraints are different.Finally, when future task arrivals cannot be precisely predicted, a deepdeterministic policy gradient (DDPG) agent is trained to call OG. Experimentsshow that IP-SSA reduces up to 94.9\% user energy consumption in the offlinesetting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92\% in the onlinesetting.

Multi-user Co-inference with Batch Processing Capable Edge Server