21 citations · 22 across the 14 of their papers we have counts for
17 papers
LevelSyn: Physical-Aware Logic Synthesis via Level-Asynchronous Graph Neural Networks
Jingyi Zhou, Zhengyuan Shi, Ziyang Zheng +1
As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic synthesis and physical design has led to significant PPA (Power, Perform…
Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits
Jingyi Zhou, Zhengyuan Shi, Jiaying Zhu +2
Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the…
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
Zeju Li, Ziyang Zheng, Yizhou Zhou +1
Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-…
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,…
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
Ruiyang Ma, Yunhao Zhou, Yipeng Wang +9
There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these model…
Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning
Zhengyuan Shi, Jingxin Wang, Wentao Jiang +5
Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast st…
TRACE: Learning to Compute on Circuit Graphs
Ziyang Zheng, Jiaying Zhu, Jingyi Zhou +1
Learning to compute, the ability to model the functional behavior of a circuit graph, is a fundamental challenge for graph representation learning. Yet, the dominant paradigm is ar…
Circuit-Aware SAT Solving: Guiding CDCL via Conditional Probabilities
Jiaying Zhu, Ziyang Zheng, Zhengyuan Shi +2
Circuit Satisfiability (CSAT) plays a pivotal role in Electronic Design Automation. The standard workflow for solving CSAT problems converts circuits into Conjunctive Normal Form (…
Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy
Zeju Li, Jianyuan Zhong, Ziyang Zheng +5
Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to incre…
ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA
Zhengyuan Shi, Zeju Li, Chengyu Ma +19
We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RT…
Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism
Ziyang Zheng, Kezhi Li, Zhengyuan Shi +1
Subgraph matching in logic circuits is foundational for numerous Electronic Design Automation (EDA) applications, including datapath optimization, arithmetic verification, and hard…
DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
Zhengyuan Shi, Chengyu Ma, Ziyang Zheng +7
We introduce DeepCell, a novel circuit representation learning framework that effectively integrates multiview information from both And-Inverter Graphs (AIGs) and Post-Mapping (PM…
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Ziyang Zheng, Shan Huang, Jianyuan Zhong +4
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT…
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
Sadaf Khan, Zhengyuan Shi, Ziyang Zheng +2
Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One not…
DeepGate3: Towards Scalable Circuit Representation Learning
Zhengyuan Shi, Ziyang Zheng, Sadaf Khan +3
Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utiliz…
The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models
Lei Chen, Yiqi Chen, Zhufei Chu +36
Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies.…
Non-Cross Diffusion for Semantic Consistency
Ziyang Zheng, Ruiyuan Gao, Qiang Xu
In diffusion models, deviations from a straight generative flow are a common issue, resulting in semantic inconsistencies and suboptimal generations. To address this challenge, we…