Recently, the applications of deep neural network (DNN) have been very
prominent in many fields such as computer vision (CV) and natural language
processing (NLP) due to its superior feature extraction performance. However,
the high-dimension parameter model and large-scale mathematical calculation
restrict the execution efficiency, especially for Internet of Things (IoT)
devices. Different from the previous cloud/edge-only pattern that brings huge
pressure for uplink communication and device-only fashion that undertakes
unaffordable calculation strength, we highlight the collaborative computation
between the device and edge for DNN models, which can achieve a good balance
between the communication load and execution accuracy. Specifically, a
systematic on-demand co-inference framework is proposed to exploit the
multi-branch structure, in which the pre-trained Alexnet is right-sized through
\emph{early-exit} and partitioned at an intermediate DNN layer. The integer
quantization is enforced to further compress transmission bits. As a result, we
establish a new Deep Reinforcement Learning (DRL) optimizer-Soft Actor Critic
for discrete (SAC-d), which generates the \emph{exit point}, \emph{partition
point}, and \emph{compressing bits} by soft policy iterations. Based on the
latency and accuracy aware reward design, such an optimizer can well adapt to
the complex environment like dynamic wireless channel and arbitrary CPU
processing, and is capable of supporting the 5G URLLC. Real-world experiment on
Raspberry Pi 4 and PC shows the outperformance of the proposed solution.

提出了一种基于 Deep Neural Network（DNN）模型的 IoT 设备与边缘协同计算框架，通过多分支结构、智能早停、硬件中间分割与整数量化等技术实现了优秀的通信负载和执行精度平衡，结合基于 Soft Actor Critic（SAC-d）的深度强化学习优化算法实现了动态无线通道和任意 CPU 处理下的适应性支持，并在树莓派 4 和 PC 上进行了实验。