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About this paper
Arian Bakhtiarnia, Nemanja Milošević, Qi Zhang, Dragana Bajović, Alexandros Iosifidis
A dynamic split computing framework is presented to optimize deep neural network inference in edge environments by adaptively distributing computation between resource-constrained devices and edge servers. This approach leverages natural bottlenecks in modern DNNs to minimize end-to-end latency under fluctuating network conditions and batch sizes, maintaining model accuracy.
Key insights
- Modern DNN architectures, such as EfficientNetV1 and V2, inherently possess multiple natural bottlenecks suitable for computation splitting.
- The optimal division point between device and server processing is highly fluid, depending significantly on real-time communication channel data rates and input batch sizes.
- A 'plug-and-play' approach can dynamically adjust this split without altering the DNN's architecture, retraining, or impacting model accuracy.
Summary by alphaXiv.
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