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Activation sparsity and dynamic pruning for split computing in edge AI
Publikationstyp
Conference Paper
Date Issued
2022-12
Sprache
English
Author(s)
Start Page
30
End Page
36
Citation
DistributedML 2022 - Proceedings of the 3rd International Workshop on Distributed Machine Learning, Part of CoNEXT 2022: 30-36
Publisher DOI
Scopus ID
Publisher
Association for Computing Machinery
ISBN
978-1-4503-9922-7
Deep neural networks are getting larger and, therefore, harder to deploy on constrained IoT devices. Split computing provides a solution by splitting a network and placing the first few layers on the IoT device. The output of these layers is transmitted to the cloud where inference continues. Earlier works indicate a degree of high sparsity in intermediate activation outputs, this paper analyzes and exploits activation sparsity to reduce the network communication overhead when transmitting intermediate data to the cloud. Specifically, we analyze the intermediate activations of two early layers in ResNet-50 on CIFAR-10 and ImageNet, focusing on sparsity to guide the process of choosing a splitting point. We employ dynamic pruning of activations and feature maps and find that sparsity is very dependent on the size of a layer, and weights do not correlate with activation sparsity in convolutional layers. Additionally, we show that sparse intermediate outputs can be compressed by a factor of 3.3X at an accuracy loss of 1.1% without any fine-tuning. When adding fine-tuning, the compression factor increases up to 14X at a total accuracy loss of 1%.
Subjects
Activation Sparsity | Deep Learning | Edge Computing | Feature Map Pruning | Offloading
MLE@TUHH
DDC Class
600: Technology