An analytical model for GPU power identifies architecture- and workload-dependent transition frequencies beyond which energy efficiency degrades, based on benchmarks on A40/A100/H100/H200 GPUs.
In: ICC 2025 - IEEE International Conference on Communications
5 Pith papers cite this work. Polarity classification is still indexing.
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An in-vehicle digital twin framework using temporal convolutional networks and hierarchical navigable small world algorithms detects Sybil attacks with 0.984 accuracy and reduces near-collision metrics by 72-88% on real-world field data.
An analytical channel impulse response for dispersive closed-loop molecular communication with pulsatile flow is derived as a wrapped normal distribution with time-variant mean and variance.
The paper surveys energy efficiency strategies for Agentic AI inference by proposing a new accounting framework and taxonomy that spans model simplification, computation control, input optimization, and cross-layer co-design with wireless networks.
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.
citing papers explorer
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Modeling and Chasing the Energy-Efficiency Sweet Spots in Modern GPUs
An analytical model for GPU power identifies architecture- and workload-dependent transition frequencies beyond which energy efficiency degrades, based on benchmarks on A40/A100/H100/H200 GPUs.
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In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection
An in-vehicle digital twin framework using temporal convolutional networks and hierarchical navigable small world algorithms detects Sybil attacks with 0.984 accuracy and reduces near-collision metrics by 72-88% on real-world field data.
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Analytical Modeling of Dispersive Closed-loop MC Channels with Pulsatile Flow
An analytical channel impulse response for dispersive closed-loop molecular communication with pulsatile flow is derived as a wrapped normal distribution with time-variant mean and variance.
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Networking-Aware Energy Efficiency in Agentic AI Inference: A Survey
The paper surveys energy efficiency strategies for Agentic AI inference by proposing a new accounting framework and taxonomy that spans model simplification, computation control, input optimization, and cross-layer co-design with wireless networks.
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Foundations of Future Communication Systems: Innovations in Communication - A Report
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.