3 citations · 4 across the 3 of their papers we have counts for
5 papers
APEX: Speculate smarter, not deeper
Manvi Jha, Zach Zhang, Zhichao Xu +3
Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness depends on both the proposal…
OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design
Jinghua Wang, Lily Jiaxin Wan, Sanjana Pingali +6
OpenRTLSet introduces the largest fully open-source dataset for hardware design, offering over 131,000 diverse Verilog code samples to the research community and industry. Our data…
Proof2Silicon: Prompt Repair for Verified Code and Hardware Generation via Reinforcement Learning
Manvi Jha, Jiaxin Wan, Deming Chen
Large Language Models (LLMs) have demonstrated impressive capabilities in automated code generation but frequently produce code that fails formal verification, an essential require…
Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs
Xingang Guo, Yaxin Li, Xiangyi Kong +62
Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. Ho…
New Solutions on LLM Acceleration, Optimization, and Application
Yingbing Huang, Lily Jiaxin Wan, Hanchen Ye +5
Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a wide range of applications. H…