  Graphics processing units (GPUs) can improve deep neural network inference
throughput via batch processing, where multiple tasks are concurrently
processed. We focus on novel scenarios that the energy-constrained mobile
devices offload inference tasks to an edge server with GPU. The inference task
is partitioned into sub-tasks for a finer granularity of offloading and
scheduling, and the user energy consumption minimization problem under
inference latency constraints is investigated. To deal with the coupled
offloading and scheduling introduced by concurrent batch processing, we first
consider an offline problem with a constant edge inference latency and the same
latency constraint. It is proven that optimizing the offloading policy of each
user independently and aggregating all the same sub-tasks in one batch is
optimal, and thus the independent partitioning and same sub-task aggregating
(IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is
proposed to optimally group tasks when the latency constraints are different.
Finally, when future task arrivals cannot be precisely predicted, a deep
deterministic policy gradient (DDPG) agent is trained to call OG. Experiments
show that IP-SSA reduces up to 94.9\% user energy consumption in the offline
setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92\% in the online
setting.
