  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.
