As deep learning models become popular, there is a lot of need for deploying them to diverse device environments. Because it is costly to develop and optimize a neural network for every single environment, there is a line of research to search neural networks for multiple target environments efficiently. However, existing works for such a situation still suffer from requiring many GPUs and expensive costs. Motivated by this, we propose a novel neural network optimization framework named Bespoke for low-cost deployment. Our framework searches for a lightweight model by replacing parts of an original model with randomly selected alternatives, each of which comes from a pretrained neural network or the original model. In the practical sense, Bespoke has two significant merits. One is that it requires near zero cost for designing the search space of neural networks. The other merit is that it exploits the sub-networks of public pretrained neural networks, so the total cost is minimal compared to the existing works. We conduct experiments exploring Bespoke's the merits, and the results show that it finds efficient models for multiple targets with meager cost.

翻译：摘要：随着深度学习模型的普及，将其部署到多样化的设备环境中的需求日益增长。由于为每种环境单独开发和优化神经网络的成本高昂，当前研究致力于高效搜索适用于多个目标环境的神经网络。然而，现有方法仍存在需要大量GPU和昂贵计算成本的问题。受此启发，我们提出一种名为Bespoke的新型神经网络优化框架，旨在实现低成本部署。该框架通过用随机选择的替代模块（源自预训练神经网络或原始模型）替换原始模型的部分结构，搜索轻量级模型。实际应用中，Bespoke具有两大显著优势：其一，设计神经网络搜索空间的成本近乎为零；其二，该方法充分利用公开预训练神经网络的子网络，使得总成本相较于现有方法显著降低。我们通过实验验证了Bespoke的优势，结果表明该框架能以极低成本为多个目标环境找到高效模型。