Pareto-Optimal Bit Allocation for Collaborative Intelligence
- 26 February 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 30 (10577149), 3348-3361
- https://doi.org/10.1109/tip.2021.3060875
Abstract
In recent studies, collaborative intelligence (CI) has emerged as a promising framework for deployment of Artificial Intelligence (AI)-based services on mobile/edge devices. In CI, the AI model (a deep neural network) is split between the edge and the cloud, and intermediate features are sent from the edge sub-model to the cloud sub-model. In this article, we study bit allocation for feature coding in multi-stream CI systems. We model task distortion as a function of rate using convex surfaces similar to those found in distortion-rate theory. Using such models, we are able to provide closed-form bit allocation solutions for single-task systems and scalarized multi-task systems. Moreover, we provide analytical characterization of the full Pareto set for 2-stream k-task systems, and bounds on the Pareto set for 3-stream 2-task systems. Analytical results are examined on a variety of DNN models from the literature to demonstrate wide applicability of the results.Keywords
Funding Information
- Natural Sciences and Engineering Research Council (RGPIN-2016-04590)
- Huawei
This publication has 37 references indexed in Scilit:
- YOLO9000: Better, Faster, StrongerPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- NeurosurgeonACM SIGARCH Computer Architecture News, 2017
- NeurosurgeonPublished by Association for Computing Machinery (ACM) ,2017
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- The Cityscapes Dataset for Semantic Urban Scene UnderstandingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Fully Convolutional Networks for Semantic SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Overview of the Range Extensions for the HEVC Standard: Tools, Profiles, and PerformanceIEEE Transactions on Circuits and Systems for Video Technology, 2015
- Overview of the High Efficiency Video Coding (HEVC) StandardIEEE Transactions on Circuits and Systems for Video Technology, 2012
- Convex OptimizationPublished by Cambridge University Press (CUP) ,2004
- The Levenberg-Marquardt algorithm: Implementation and theoryPublished by Springer Science and Business Media LLC ,1978