This paper studies inference acceleration using distributed convolutional
neural networks (CNNs) in collaborative edge computing network. To avoid
inference accuracy loss in inference task partitioning, we propose receptive
field-based segmentation (RFS). To reduce the computation time and
communication overhead, we propose a novel collaborative edge computing using
fused-layer parallelization to partition a CNN model into multiple blocks of
convolutional layers. In this scheme, the collaborative edge servers (ESs) only
need to exchange small fraction of the sub-outputs after computing each fused
block. In addition, to find the optimal solution of partitioning a CNN model
into multiple blocks, we use dynamic programming, named as dynamic programming
for fused-layer parallelization (DPFP). The experimental results show that DPFP
can accelerate inference of VGG-16 up to 73% compared with the pre-trained
model, which outperforms the existing work MoDNN in all tested scenarios.
Moreover, we evaluate the service reliability of DPFP under time-variant
channel, which shows that DPFP is an effective solution to ensure high service
reliability with strict service deadline.

本文研究使用卷积神经网络在协作边缘计算网络中进行推理加速，提出了使用基于感受野的分割来避免推理任务划分中的推理准确度损失，提出了一种新颖的合作式边缘计算方法，使用融合层并行化将 CNN 模型划分为多个卷积层块，使用 DPFP 动态规划找到将 CNN 模型划分为多个块的最优解，实验结果表明，DPFP 可以将 VGG-16 的推理加速高达 73％，服务可靠性高。