Dynamic DNN partitioning refers to the process of dividing a Deep Neural Network (DNN) into smaller subnetworks that can be executed on different hardware devices or processors. This approach aims to overcome the limitations of using a single device to execute a large and complex neural network.
The partitioning process involves identifying the layers or units of the DNN that can be grouped together and executed on a specific hardware device. The goal is to optimize the execution time and minimize communication between devices. Different partitioning algorithms have been proposed, including graph-based partitioning, layer-wise partitioning, and weight clustering.
Dynamic DNN partitioning allows for better utilization of hardware resources, especially in distributed systems where multiple devices are available. It also enables the deployment of DNN models in resource-constrained environments such as mobile devices and embedded systems.
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