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Found 3 papers, 1 papers with code
The Effects of Partitioning Strategies on Energy Consumption in Distributed CNN Inference at The Edge
Nowadays, many AI applications utilizing resource-constrained edge devices (e. g., small mobile robots, tiny IoT devices, etc.)
AutoDiCE: Fully Automated Distributed CNN Inference at the Edge
Although such distribution, deployment, and execution of large CNNs on multiple edge devices is a desirable and beneficial approach, there currently does not exist a design and programming framework that takes a trained CNN model, together with a CNN partitioning specification, and fully automates the CNN model splitting and deployment on multiple edge devices to facilitate distributed CNN inference at the Edge.
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