3 papers
cs.LG2026
Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation
Andrew S. Cassidy, Guillaume Garreau, Jay Sivagnaname +4
We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain (for example, Hardware Description Lan…
cs.DC2025
A Scalable NorthPole System with End-to-End Vertical Integration for Low-Latency and Energy-Efficient LLM Inference
Michael V. DeBole, Rathinakumar Appuswamy, Neil McGlohon +30
A vertically integrated, end-to-end, research prototype system combines 288 NorthPole neural inference accelerator cards, offline training algorithms, a high-performance runtime st…
cs.LG2025
SiLQ: Simple Large Language Model Quantization-Aware Training
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani +2
Large language models can be quantized to reduce inference time latency, model size, and energy consumption, thereby delivering a better user experience at lower cost. A challenge…