Split Computing With Scalable Feature Compression for Visual Analytics on the Edge

Running deep visual analytics models for real-time applications is challenging for mobile devices. Offloading the computation to edge server can mitigate computation bottleneck at the mobile device, but may decrease the analytics performance due to the necessity of compressing the image data. We consider a "split computing" system to offload a part of the deep learning model's computation and introduce a novel learned feature compression approach with lightweight computation. We demonstrate the effectiveness of the split computing pipeline in performing computation offloading for the problems of object detection and image classification. Compared to compressing the raw images at the mobile, and running the analytics model on the decompressed images at the server, the proposed feature-compression approach can achieve significantly higher analytics performance at the same bit rate, while reducing the complexity at the mobile. We further propose a scalable feature compression approach, which facilitates adaptation to network bandwidth dynamics, while having comparable performance to the non-scalable approach.