10 papers
UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
Yi Liu, Hongji Zhang, Lei Chen +2
Developing effective representations for register transfer level (RTL) designs is crucial for accelerating the hardware design workflow. Existing approaches, however, typically rel…
Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
Yi Liu, Hongji Zhang, Yiwen Wang +4
Estimating the quality of register transfer level (RTL) designs is crucial in the electronic design automation (EDA) workflow, as it enables instant feedback on key performance met…
Context Distillation as Latent Memory Management
Ziyang Zheng, Zeju Li, Xiangyu Wen +5
Context distillation compresses contextual information into model parameters, yet existing methods often ignore how multiple distilled latent memories should be stored, retrieved,…
Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
Zixuan Huang, Da Chen, Kecheng Huang +5
Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language models (LLMs) show promise in…
SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding
Chengxi Li, Yang Sun, Lei Chen +3
This paper proposes smaRTLy: a new optimization technique for multiplexers in Register-Transfer Level (RTL) logic synthesis. Multiplexer trees are very common in RTL designs, and t…
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Haoyang Li, Zhanchao Xu, Yiming Li +9
Multi-turn dialogues are essential in many real-world applications of large language models, such as chatbots and virtual assistants. As conversation histories become longer, exist…