Splitting Convolutional Neural Network Structures for Efficient Inference
This addresses memory management issues for deploying CNNs on resource-constrained devices, but it is incremental as it builds on existing splitting methods.
The paper tackles the problem of high memory usage in convolutional neural networks (CNNs) by proposing a network structure splitting technique, which reduces computational operations and memory consumption when tested on VGG16 and ResNet18 for CIFAR10 classification.
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.