  Industrial Internet of Things (IIoT) applications can benefit from leveraging
edge computing. For example, applications underpinned by deep neural networks
(DNN) models can be sliced and distributed across the IIoT device and the edge
of the network for improving the overall performance of inference and for
enhancing privacy of the input data, such as industrial product images.
However, low network performance between IIoT devices and the edge is often a
bottleneck. In this study, we develop ScissionLite, a holistic framework for
accelerating distributed DNN inference using the Transfer Layer (TL). The TL is
a traffic-aware layer inserted between the optimal slicing point of a DNN model
slice in order to decrease the outbound network traffic without a significant
accuracy drop. For the TL, we implement a new lightweight down/upsampling
network for performance-limited IIoT devices. In ScissionLite, we develop
ScissionTL, the Preprocessor, and the Offloader for end-to-end activities for
deploying DNN slices with the TL. They decide the optimal slicing point of the
DNN, prepare pre-trained DNN slices including the TL, and execute the DNN
slices on an IIoT device and the edge. Employing the TL for the sliced DNN
models has a negligible overhead. ScissionLite improves the inference latency
by up to 16 and 2.8 times when compared to execution on the local device and an
existing state-of-the-art model slicing approach respectively.
