Pareto-Optimal Bit Allocation for Collaborative Intelligence
- URL: http://arxiv.org/abs/2009.12430v2
- Date: Thu, 29 Apr 2021 23:41:16 GMT
- Title: Pareto-Optimal Bit Allocation for Collaborative Intelligence
- Authors: Saeed Ranjbar Alvar and Ivan V. Baji\'c
- Abstract summary: Collaborative intelligence (CI) has emerged as a promising framework for deployment of Artificial Intelligence (AI)-based services on mobile/edge devices.
In this paper, we study bit allocation for feature coding in multi-stream CI systems.
- Score: 39.11380888887304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 paper, 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
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