In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing.
翻译:近年来,深层学习模式在产业和学术界都变得无处不在。现代深层神经网络可以解决当今最复杂的问题之一,但价格是巨大的计算和存储要求。这使得部署如此庞大的神经网络在移动边缘计算模式中具有挑战性,因为边缘节点受到资源限制,从而限制了这种框架的投入分析力。用于分布式处理的神经网络的静态和分层分解显示了这方面的希望。然而,没有智能算法将这种模块分割作为优化性能的边缘节点。 这项工作提出了一个新的配置政策 — — SplitPlace, 用于将这种神经网络的分裂碎片放置在移动边缘主机上,以便高效和可缩放的计算。