Supervised Compression for Split Computing
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.
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.
SC2 Benchmark: Supervised Compression for Split Computing
Transactions on Machine Learning Research (TMLR)
This work was done prior to joining Amazon.OpenReview
SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing
This work was done prior to joining Amazon.Code
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
ACM Computing Surveys (CSUR)
Supervised Compression for Resource-Constrained Edge Computing Systems
Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs
IEEE GLOBECOM 2020Paper
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-constrained Edge Computing Systems
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
MobiCom 2020 Workshop on Embedded and Mobile Deep Learning (EMDL)
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
MobiCom 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo)Paper