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