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Unlike cloud-based deep learning models that are often large and uniform,edge-deployed models usually demand customization for domain-specific tasks andresource-limited environments. Such customization processes can be costly andtime-consuming due to the diversity of edge scenarios and the training load foreach scenario. Although various approaches have been proposed for rapidresource-oriented customization and task-oriented customization respectively,achieving both of them at the same time is challenging. Drawing inspirationfrom the generative AI and the modular composability of neural networks, weintroduce NN-Factory, an one-for-all framework to generate customizedlightweight models for diverse edge scenarios. The key idea is to use agenerative model to directly produce the customized models, instead of trainingthem. The main components of NN-Factory include a modular supernet withpretrained modules that can be conditionally activated to accomplish differenttasks and a generative module assembler that manipulate the modules accordingto task and sparsity requirements. Given an edge scenario, NN-Factory canefficiently customize a compact model specialized in the edge task whilesatisfying the edge resource constraints by searching for the optimal strategyto assemble the modules. Based on experiments on image classification andobject detection tasks with different edge devices, NN-Factory is able togenerate high-quality task- and resource-specific models within few seconds,faster than conventional model customization approaches by orders of magnitude.

Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints