30 citations · 41 across the 8 of their papers we have counts for
8 papers
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,…
FormalRTL: Verified RTL Synthesis at Scale
Kezhi Li, Min Li, Xiangyu Wen +4
Large language models (LLMs) have demonstrated significant potential in automating hardware synthesis, yet substantial barriers remain for industrial-scale, datapath-centric design…
Reasoning Scaffolding: Distilling the Flow of Thought from LLMs
Xiangyu Wen, Junhua Huang, Zeju Li +6
The prevailing approach to distilling reasoning from Large Language Models (LLMs)-behavioral cloning from textual rationales-is fundamentally limited. It teaches Small Language Mod…
VerilogReader: LLM-Aided Hardware Test Generation
Ruiyang Ma, Yuxin Yang, Ziqian Liu +4
Test generation has been a critical and labor-intensive process in hardware design verification. Recently, the emergence of Large Language Model (LLM) with their advanced understan…
IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability
Tsz Ho Chan, Wenyi Xiao, Junhua Huang +3
Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employe…
DeepGate2: Functionality-Aware Circuit Representation Learning
Zhengyuan Shi, Hongyang Pan, Sadaf Khan +7
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and l…
Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG
Hui-Ling Zhen, Naixing Wang, Junhua Huang +3
Due to the increasing challenges posed by the relentless rise in the design complexity of integrated circuits, Boolean Satisfiability (SAT) has emerged as a robust alternative to s…
SATformer: Transformer-Based UNSAT Core Learning
Zhengyuan Shi, Min Li, Yi Liu +5
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the…