Author

Jerry Wu

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Jul 2026

Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Serving

As neural processing units (NPUs) evolve rapidly to accommodate the ever-increasing compute demand of large language models (LLMs), their power consumption is becoming a limiting factor. Our study shows that using dynamic voltage and frequency scaling (DVFS) to exploit the service-level objective (SLO) slacks is a prom...

Yu Xue, Jerry Wu, Corey Yu et al. · arXiv.org · 0 citations

Preprint Aug 2026

Launch-Bound and Substitutable: Why Three Inference Optimizations Fail to Pay Off in Mixture-of-Experts Models

A fourth result ties the three together: leaving the routers in FP16 lowers drift by 20% while raising loss, so routing fidelity and output quality are separable objectives.

Gokulakannan Sakthivel, Jerry Wu, A. Rajendra et al. · 0 citations

Jul 2026

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts

It is shown that post-training quantization can silently alter how large language models reason even when task accuracy is preserved, and Hollow Convergence is established as a deployment-relevant failure mode that standard evaluation pipelines cannot catch.

Renuka Oladri, Mohana Priya, Jerry Wu · arXiv.org · 0 citations