2 papers
cs.AR2026
Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Serving
Yuqi Xue, Jerry Wu, Corey Yu +1
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 f…
cs.CL2026
Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts
Renuka Oladri, Mohan Vamsi Varadaraju Priya, Jerry Wu
We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Using a six-category failure taxonomy validated by…