A Linear NAS Service of ConvNets for Fast Deployment in the Edge of 5G Networks

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.