The authors prove supply-demand converses and a receiver-internal compute min-cut bound for reliable remote inference under unreliable communication and computation, showing that certain losses are closure-dependent rather than universal.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
On Grace Hopper superchips, energy efficiency during multimodal training is governed by data movement and overlap rather than compute utilization, and runtime-optimal configurations are not always energy-optimal.
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Reliable Remote Inference from Unreliable Components: Joint Communication and Computation Limits
The authors prove supply-demand converses and a receiver-internal compute min-cut bound for reliable remote inference under unreliable communication and computation, showing that certain losses are closure-dependent rather than universal.
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Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
On Grace Hopper superchips, energy efficiency during multimodal training is governed by data movement and overlap rather than compute utilization, and runtime-optimal configurations are not always energy-optimal.