The 5G network brings about significant convenience in deploying neural network models to edge devices. However, the flexibility challenges the current generation of neural network architectures that seldom involve the target platform as the bounds during the structure searching. The difficult deployment is rooted in the hidden resource provision of the target devices during the SW/HW joint tuning, leading to mismatching between network models and system configuration. This work proposes a scalable neural network search service in the 5G environment to support a continuous knob of the network scales, by which the channel groups can overlap with each other to share the features with continuous coverage. It is proved that the proposed dimension of network scaling provides good predictability of the model performance on specific platforms, which can be further utilized to simplify the regular network architectural search procedures. Then we design a SW/HW co-design workflow that involves both the cloud and edge to fully utilize computing resources on target platforms, meanwhile keeping the network size as small as possible to save the provision of resources. The experimental results show that, with our scalable search service, the key metrics of the network model enjoy a continuous, monotonic, and linear function to the proposed hyper-parameter. The deployment to the Raspberry Pi board shows that the proposed method accurately controls both precision and size of the models; meanwhile, the corresponding search workflow successfully finds the proper network scales with 65 percent reduction of the NAS routines.

中文翻译：

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ConvNets的线性NAS服务，可在5G网络边缘快速部署

5G网络为将神经网络模型部署到边缘设备带来了极大的便利。但是，灵活性挑战了当前的神经网络体系结构，在结构搜索过程中，神经网络体系结构很少涉及目标平台。困难的部署源于在SW / HW联合调整期间目标设备的隐藏资源提供，从而导致网络模型与系统配置之间的不匹配。这项工作提出了在5G环境中的可扩展神经网络搜索服务，以支持网络规模的连续调节，通过该调节，通道组可以彼此重叠以共享具有连续覆盖范围的特征。事实证明，建议的网络扩展维度可为特定平台上的模型性能提供良好的可预测性，可以进一步利用它简化常规的网络体系结构搜索过程。然后，我们设计一个包含云和边缘的软件/硬件协同设计工作流程，以充分利用目标平台上的计算资源，同时保持网络规模尽可能小，以节省资源。实验结果表明，利用我们的可扩展搜索服务，网络模型的关键指标对于拟议的超参数具有连续，单调和线性的功能。在Raspberry Pi板上的部署表明，该方法可以精确控制模型的精度和大小。同时，相应的搜索工作流程成功地找到了合适的网络规模，而NAS例程减少了65％。