Splitting Convolutional Neural Network Structures for Efficient
Inference
Academic Article
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Overview
abstract
For convolutional neural networks (CNNs) that have a large volume of input
data, memory management becomes a major concern. Memory cost reduction can be
an effective way to deal with these problems that can be realized through
different techniques such as feature map pruning, input data splitting, etc.
Among various methods existing in this area of research, splitting the network
structure is an interesting research field, and there are a few works done in
this area. In this study, the problem of reducing memory utilization using
network structure splitting is addressed. A new technique is proposed to split
the network structure into small parts that consume lower memory than the
original network. The split parts can be processed almost separately, which
provides an essential role for better memory management. The split approach has
been tested on two well-known network structures of VGG16 and ResNet18 for the
classification of CIFAR10 images. Simulation results show that the splitting
method reduces both the number of computational operations as well as the
amount of memory consumption.