Projects

This page is a showcase of OSS (open source software) and papers which have used sc2bench in the projects. If your work is built on sc2bench, start a β€œShow and tell” discussion at GitHub.

Papers

FrankenSplit: Efficient Neural Feature Compression With Shallow Variational Bottleneck Injection for Mobile Edge Computing

  • Author(s): Alireza Furutanpey, Philipp Raith, Schahram Dustdar

  • Venue: IEEE Transactions on Mobile Computing

  • PDF: Paper

  • Code: GitHub

Abstract: The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side. However, critical applications require powerful models that edge devices cannot host and must therefore offload requests, where the high-dimensional data will compete for limited bandwidth. Split Computing (SC) alleviates resource inefficiency by partitioning DNN layers across devices, but current methods are overly specific and only marginally reduce bandwidth consumption. This work proposes shifting away from focusing on executing shallow layers of partitioned DNNs. Instead, it advocates concentrating the local resources on variational compression optimized for machine interpretability. We introduce a novel framework for resource-conscious compression models and extensively evaluate our method in an environment reflecting the asymmetric resource distribution between edge devices and servers. Our method achieves 60% lower bitrate than a state-of-the-art SC method without decreasing accuracy and is up to 16x faster than offloading with existing codec standards.

SC2 Benchmark: Supervised Compression for Split Computing

  • Author(s): Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

  • Venue: TMLR

  • PDF: Paper + Supp

  • Code: GitHub

Abstract: With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attractive solution. However, existing split computing approaches often underperform compared to a naive baseline of remote computation on compressed data. Recent studies propose learning compressed representations that contain more relevant information for supervised downstream tasks, showing improved tradeoffs between compressed data size and supervised performance. However, existing evaluation metrics only provide an incomplete picture of split computing. This study introduces supervised compression for split computing (SC2) and proposes new evaluation criteria: minimizing computation on the mobile device, minimizing transmitted data size, and maximizing model accuracy. We conduct a comprehensive benchmark study using 10 baseline methods, three computer vision tasks, and over 180 trained models, and discuss various aspects of SC2. We also release our code and sc2bench, a Python package for future research on SC2. Our proposed metrics and package will help researchers better understand the tradeoffs of supervised compression in split computing.