CATS enables collaborative transformer inference on up to 16 ultra-low-power wireless devices, supporting models up to 14 times larger than a single device can run via SomeGather pruning and message-dropout robustness.
Communication- efficient distributed on-device LLM inference over wireless networks.arXiv preprint arXiv:2503.14882
2 Pith papers cite this work. Polarity classification is still indexing.
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A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.
citing papers explorer
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Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices
CATS enables collaborative transformer inference on up to 16 ultra-low-power wireless devices, supporting models up to 14 times larger than a single device can run via SomeGather pruning and message-dropout robustness.
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Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs and LLM integration.