Zhengyuan Shi
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View article: DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior Open
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 models fail to capture circuit runtime behavior, which is crucia…
View article: Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning
Alignment Unlocks Complementarity: A Framework for Multiview Circuit Representation Learning Open
Multiview learning on Boolean circuits holds immense promise, as different graph-based representations offer complementary structural and semantic information. However, the vast structural heterogeneity between views, such as an And-Invert…
View article: Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism
Functional Matching of Logic Subgraphs: Beyond Structural Isomorphism Open
Subgraph matching in logic circuits is foundational for numerous Electronic Design Automation (EDA) applications, including datapath optimization, arithmetic verification, and hardware trojan detection. However, existing techniques rely pr…
View article: ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA
ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA Open
We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RTL) code, Post-mapping (PM) netlists, And-Inverter Graphs (A…
View article: DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning Open
We introduce DeepCell, a novel circuit representation learning framework that effectively integrates multiview information from both And-Inverter Graphs (AIGs) and Post-Mapping (PM) netlists. At its core, DeepCell employs a self-supervised…
View article: DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale Open
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT solving. However, existing models face significant challen…
View article: DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations Open
View article: DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations
DeepSeq2: Enhanced Sequential Circuit Learning with Disentangled Representations Open
Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One notable work, DeepSeq, has pioneered sequential circuit learni…
View article: DeepGate3: Towards Scalable Circuit Representation Learning
DeepGate3: Towards Scalable Circuit Representation Learning Open
View article: DeepGate3: Towards Scalable Circuit Representation Learning
DeepGate3: Towards Scalable Circuit Representation Learning Open
Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utilize Graph Neural Networks (GNNs) to encode circuit netlists i…
View article: Customized FPGA Implementation of Authenticated Lightweight Cipher Fountain for IoT Systems
Customized FPGA Implementation of Authenticated Lightweight Cipher Fountain for IoT Systems Open
Authenticated Encryption with Associated-Data (AEAD) can ensure both confidentiality and integrity of information in encrypted communication. Distinctive variants are customized from AEAD to satisfy various requirements. In this paper, we …
View article: DeepGate2: Functionality-Aware Circuit Representation Learning
DeepGate2: Functionality-Aware Circuit Representation Learning Open
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 logic reasoning tasks. Existing solutions, such as DeepGate,…
View article: Addressing Variable Dependency in GNN-based SAT Solving
Addressing Variable Dependency in GNN-based SAT Solving Open
Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions concurrently. We …
View article: DeepSeq: Deep Sequential Circuit Learning
DeepSeq: Deep Sequential Circuit Learning Open
Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet effective representation can help to solve multiple downstrea…
View article: SATformer: Transformer-Based UNSAT Core Learning
SATformer: Transformer-Based UNSAT Core Learning Open
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the problem from the opposite direction by focusing on unsatis…
View article: DeepTPI: Test Point Insertion with Deep Reinforcement Learning
DeepTPI: Test Point Insertion with Deep Reinforcement Learning Open
Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinf…
View article: DeepSAT: An EDA-Driven Learning Framework for SAT
DeepSAT: An EDA-Driven Learning Framework for SAT Open
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the we…
View article: DeepGate: Learning Neural Representations of Logic Gates
DeepGate: Learning Neural Representations of Logic Gates Open
Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they r…
View article: Multi-Reconstruction from Points Cloud by Using a Modified Vector-Valued Allen–Cahn Equation
Multi-Reconstruction from Points Cloud by Using a Modified Vector-Valued Allen–Cahn Equation Open
The Poisson surface reconstruction algorithm has become a very popular tool of reconstruction from point clouds. If we reconstruct each region separately in the process of multi-reconstruction, then the reconstructed objects may overlap wi…