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Large circuit models: opportunities and challenges

science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Learnable Flow Model Conditioned on Graph Representation Memory for Anomaly Detection 48

Learnable Flow Model Conditioned on Graph Representation Mem...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhu, Ziyu Liu, Wenlei Deng, Zhidong Tsinghua University Department of Computer Science and Technology Beijing100084 China State Key Laboratory of Intelligent Technology and Systems China

ISBN: (纸本)9781728163277

Anomaly detection could be applied in a wide range of fields from industrial scene to medical imaging analysis. Although invertible flow models are developed to accomplish unsupervised anomaly detection, they are usually hard to train and have limited capabilities of accurately modeling the distribution of normal samples. To address this problem, we propose a novel enhanced flow model conditioned on graph representation memory (FlowGRM) for visual surface defect detection. In our FlowGRM, a graph neural network is trained to query graph embedding features from memory bank and incorporate them into a learnable flow model as conditional information. Such memorized conditional information, together with the invertible flow model, is jointly optimized to give rise to a probability density score. Experimental results obtained on the industrial visual anomaly detection datasets including MVTec and MTD show that the proposed model has state-of-the-art performance, which could even reach up to 99.6% and 99.2% AUROC scores on the above two bench-marks, respectively. Meanwhile, it requires fewer parameters and saves over 80% training time compared to existing flow models. © 2023 IEEE.

关键词： Anomaly detection

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A Performance Evaluation Method for a Class of Cross-Chain Systems 12th

A Performance Evaluation Method for a Class of Cross-Chai...

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12th International Conference on Mobile Networks and Management, MONAMI 2022

作者： Wu, Ou Huang, Binbin Li, Shanshan Wang, Yanze Li, Haoming State Key Laboratory of Novel Software Technology Software Institute Nanjing University Nanjing China Department of Basic Courses The PLA Army Engineering University Nanjing China School of Computer Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9783031324420

In recent years, the heterogeneity among the blockchains has become the driving force behind the development of cross-chain technologies. Due to the limited processing speed of cross-chain system, excessive cross-chain transactions in the short term may cause network congestion and negatively impact. For this reason, it is essential to evaluate and optimize the performance of the cross-blockchain transaction process. However, existing research ignores the limitations of cross-chain systems. Much research is carried out to model, simulate and analyze the performance of traditional blockchain systems rather than cross-blockchain processes. To bridge this gap, our study proposes a queuing theoretical model based on system finite space, using the case of Cosmos, a typical cross-blockchain implemented by the relay mode. The solution of the steady-state equations are established by two-dimensional continuous time Markov process, and the performance measures such as average queue length, transaction rejection probability, and transaction response time are given. Finally, we simulated the analytical solutions of the relevant performance measures through experiments to verify the model’s effectiveness. We believe this analytical approach can be generalized to other cross-blockchain systems. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Queueing theory

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MiTAR:a study on human activity recognition based on NLP with microscopic perspective

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Frontiers of computer science 2021年第5期15卷 93-110页

作者： Huichao MEN Botao WANG Gang WU School of Computer Science and Engineering Northeastern UniversityShenyang 110000China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210000China

Nowadays,human activity recognition is becoming a more and more significant topic,and there is also a wide range of applications for it in real world *** data is an important data source in engineering and *** present,some studies have been carried out in the field of human activity recognition based on sensor data in a macroscopic ***,many studies in this perspective face some *** pivotal limitation is uncontrollable data segment length of different kinds of *** feature and data form are also influencing *** paper carries out the study creatively on a microscopic perspective with an emphasis on the logic and relevance between data segments,attempting to apply the idea of natural language processing and the method of data symbolization to the study of human activity recognition and try to solve the problem *** this paper,several activity-element definitions and three algorithms are proposed,including the algorithm of dictionary building,the algorithm of corpus building,and activity recognition algorithm improved from a natural language analysis method,*** experiments on different aspects of this model are *** experiments are carried out on six complex and representative single-level sensor datasets,namely UCI Sports and Daily dataset,Skoda dataset,WISDM Phoneacc dataset,WISDM Watchacc dataset,Healthy Older People dataset and HAPT dataset,which prove that this model can be applied to different datasets and achieve a satisfactory recognition result.

关键词： human activity recognition NLP time series data analysis data symbolization

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Safe and Robust Subgame Exploitation in Imperfect Information Games 41

Safe and Robust Subgame Exploitation in Imperfect Informatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Ge, Zhenxing Xu, Zheng Ding, Tianyu Meng, Linjian An, Bo Li, Wenbin Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore Microsoft Corporation RedmondWA United States Skywork AI Singapore School of Intelligence Science and Technology Nanjing University Suzhou Campus Jiangsu Suzhou China

Opponent exploitation is an important task for players to exploit the weaknesses of others in games. Existing approaches mainly focus on balancing between exploitation and exploitability but are often vulnerable to modeling errors and deceptive adversaries. To address this problem, our paper offers a novel perspective on the safety of opponent exploitation, named Adaptation Safety. This concept leverages the insight that strategies, even those not explicitly aimed at opponent exploitation, may inherently be exploitable due to computational complexities, rendering traditional safety overly rigorous. In contrast, adaptation safety requires that the strategy should not be more exploitable than it would be in scenarios where opponent exploitation is not considered. Building on such adaptation safety, we further propose an Opponent eXploitation Search (OX-Search) framework by incorporating real-time search techniques for efficient online opponent exploitation. Moreover, we provide theoretical analyses to show the adaptation safety and robust exploitation of OX-Search, even with inaccurate opponent models. Empirical evaluations in popular poker games demonstrate OX-Search's superiority in both exploitability and exploitation compared to previous methods. Copyright 2024 by the author(s)

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Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection 12

Dynamic Graph Learning with Long and Short-Term for Multivar...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Tian, Yuyin Gao, Rong Yan, Lingyu Liu, Donghua Ye, Zhiwei School of Computer Science Hubei University of Technology Wuhan430068 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China China Waterborne Transport Research Institue Information Center Beijing100080 China

ISBN: (纸本)9798350358056

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets. © 2023 IEEE.

关键词： Anomaly Detection Graph Learning Multivariate Time Series Time Convolution

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Yi Liu, Jianxun Zhang, Xiangping Hu, Haize Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan411201 China Hunan University of Humanities Science and Technology School of Informationn Loudi417000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350304855

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today's integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task. © 2023 IEEE.

关键词： Neural network models

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Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions 10th

Enhancing Multi-modal Regular Expression Synthesis via Larg...

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10th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2024

作者： Tang, Zipan Yan, Yixuan Li, Rongchen Dong, Hanze Chen, Haiming Gao, Hongyu Beijing University of Technology Beijing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Real-world regular expressions (regexes) are widely used in practice. Since regexes are difficult to comprehend and write, automatically synthesizing regexes has been an important research problem. However, current techniques face challenges like weak capability in generalization and severely limited support of extended features of regexes. In this paper, we address these challenges via Large Language Models (LLMs) together with semantic manipulations of sub-expressions, and propose PowerSyn, a framework for regex synthesis based on both natural language descriptions and examples and supports extended features. Specifically, we design the prompt suitable for synthesizing regexes with LLMs and present novel algorithms to manipulate sub-expressions, which consider the matching relation, and are guided by examples. The evaluation results demonstrate the significant efficacy of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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BonsaiKV: Towards Fast, Scalable, and Persistent key-Value Stores with Tiered, Heterogeneous Memory System 50th

BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value S...

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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Cai, Miao Shen, Junru Yuan, Yifan Qu, Zhihao Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University School of Computer and Information Hohai University China School of Computer and Information Hohai University China Intel Labs China State Key Laboratory for Novel Software Technology Nanjing University China

Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To fully unleash the performance potential of such memory systems, this paper presents BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. The core of BonsaiKV is a tri-layer hierarchical storage architecture that separates data indexing, persistence, and scalability from each other and realizes each of them within a specialized software-hardware layer. We design BonsaiKV with a set of novel techniques, including collaborative tiered indexing, NVMM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management, to leverage hardware strengths and tackle device deficiencies. We compare BonsaiKV with state-of-the-art NVMM-optimized key-value stores and persistent index structures using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV outperforms others by up to 7.69×, 19.59×, and 12.86× in read-, write- and scan-intensive scenarios, respectively. © 2023, VLDB Endowment. All rights reserved.

关键词： Dynamic random access storage

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Low-Power Beamforming Design for Near-Field Integrated Sensing and Communication Networks

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IEEE Internet of Things Journal 2024年第11期12卷 14979-14992页

作者： Cai, Ziwei Sheng, Min Liu, Jia Liu, Junyu Li, Jiandong Xidian University School of Computer Science and Technology Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Network Xi'an710071 China Peng Cheng Laboratory Department of Broadband Communication Guangdong Shenzhen518000 China

Integrated Sensing and Communication (ISAC) has emerged as a cornerstone technology for achieving seamless coverage in next-generation networks. Moreover, the advent of extremely large-scale multiple-input multiple-output significantly enhances ISAC's potential, facilitating innovative applications in near-field ISAC. Nonetheless, ISAC networks face numerous challenges, with power consumption being one of the most critical concerns. To address this issue, we propose a novel low-power beamforming approach within the Coordinated Multi-Point (CoMP) ISAC framework. Specifically, our approach involves orchestrating base station cooperation for seamless coverage and synergistically augmenting the sensing beam with the communication beam to reduce power consumption. By utilizing near-field communication theory, we accurately model signal propagation dynamics and formulate a beamforming optimization problem aimed at minimizing transmit power while adhering to transmission rate and object detection constraints, which is a non-convex second-order cone programming problem. To overcome the non-convexity of this problem, we propose a Successive Convex Approximation (SCA)-based beamforming optimization algorithm that ensures convergence. Moreover, we propose a fast-converging algorithm that leverages the unique characteristics of both communication channel and sensing array response vector. Simulation results validate the effectiveness of the proposed scheme for the power minimization problem and yield essential design insights. © 2014 IEEE.

关键词： Near field communication

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