  Recently, deploying deep neural network (DNN) models via collaborative
inference, which splits a pre-trained model into two parts and executes them on
user equipment (UE) and edge server respectively, becomes attractive. However,
the large intermediate feature of DNN impedes flexible decoupling, and existing
approaches either focus on the single UE scenario or simply define tasks
considering the required CPU cycles, but ignore the indivisibility of a single
DNN layer. In this paper, we study the multi-agent collaborative inference
scenario, where a single edge server coordinates the inference of multiple UEs.
Our goal is to achieve fast and energy-efficient inference for all UEs. To
achieve this goal, we first design a lightweight autoencoder-based method to
compress the large intermediate feature. Then we define tasks according to the
inference overhead of DNNs and formulate the problem as a Markov decision
process (MDP). Finally, we propose a multi-agent hybrid proximal policy
optimization (MAHPPO) algorithm to solve the optimization problem with a hybrid
action space. We conduct extensive experiments with different types of
networks, and the results show that our method can reduce up to 56\% of
inference latency and save up to 72\% of energy consumption.
