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    Collaborative intelligence (CI) optimizes Artificial Intelligence (AI) on mobile devices by splitting deep neural networks. This study provides bit allocation solutions for efficient feature coding in multi-stream CI systems.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Collaborative intelligence (CI) is a framework for deploying Artificial Intelligence (AI) services on mobile/edge devices.
    • CI splits AI models, specifically deep neural networks, between edge and cloud for efficient processing.
    • Intermediate features are transmitted from the edge sub-model to the cloud sub-model.

    Purpose of the Study:

    • To investigate and optimize bit allocation for feature coding in multi-stream CI systems.
    • To develop analytical models for task distortion as a function of rate in CI.
    • To provide efficient bit allocation solutions for various CI system configurations.

    Main Methods:

    • Modeling task distortion as a function of rate using convex surfaces, drawing parallels with distortion-rate theory.
    • Deriving closed-form bit allocation solutions for single-task and scalarized multi-task CI systems.
    • Analytically characterizing the Pareto set for 2-stream k-task systems and establishing bounds for 3-stream 2-task systems.

    Main Results:

    • Closed-form bit allocation solutions were obtained for single-task and scalarized multi-task CI systems.
    • Analytical characterization of the Pareto set for 2-stream k-task systems was achieved.
    • Bounds for the Pareto set in 3-stream 2-task systems were established, demonstrating wide applicability across various DNN models.

    Conclusions:

    • The study provides effective bit allocation strategies for feature coding in multi-stream CI systems.
    • The developed analytical models and solutions are broadly applicable to diverse deep neural network architectures.
    • This research contributes to the efficient deployment of AI on edge devices through collaborative intelligence frameworks.