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.
A survey of graph-based resource management in wireless networks - part II: Learning approaches,
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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.