Supervised Compression for Split Computing

Summary

Split computing has been attracting attentions from the research community as an intermediate option between local (mobile) computing and full computation offloading. This approach is effective especially for resource-constrained edge computing systems e.g., computationally weak mobile devices, limited wireless communication capacity. We proposed introducing bottleneck (splitting point) to deep learning models at their early stages so that we can minimize local computing and data communication costs while maximizing model accuracy, which we formulated later as supervised compression for split computing (SC2).

We define supervised compression as learning compressed representations for supervised downstream tasks. It is very challenging to introduce bottlenecks (splitting points) to deep learning models at their early layers while preserving model accuracy. SC2 is a key to achieve efficient and accuracy machine learning applications for resource-constrained edge computing systems.

ML OSS

To facilitate research on supervised compression for split computing (SC2), I published an installable Python package named sc2bench (i.e., "pip3 install sc2bench") and a code repository to reproduce the experimental results reported in our SC2 paper.

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