SLIDE enables simultaneous layer-by-layer model downloading and inference to maximize task throughput in multi-user wireless systems through joint optimization of provisioning, spectrum, and computing resources.
Efficient parallel split learning over resource-constrained wireless edge networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Models arbitrary AI models as DAGs and solves split-learning model partitioning via min s-t cut / max-flow equivalence, plus a low-complexity block-wise variant, with hardware experiments showing up to 13x faster decisions and 39% lower delay.
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SLIDE: Simultaneous Model Downloading and Inference at the Wireless Network Edge
SLIDE enables simultaneous layer-by-layer model downloading and inference to maximize task throughput in multi-user wireless systems through joint optimization of provisioning, spectrum, and computing resources.
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Fast AI Model Partition for Split Learning over Edge Networks
Models arbitrary AI models as DAGs and solves split-learning model partitioning via min s-t cut / max-flow equivalence, plus a low-complexity block-wise variant, with hardware experiments showing up to 13x faster decisions and 39% lower delay.