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Quingo: A programming framework for heterogeneous quantum-classical computing with NISQ features

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arXiv 2020年

作者： Fu, Xiang Yu, Jintao Su, Xing Jiang, Hanru Wu, Hua Cheng, Fucheng Deng, Xi Zhang, Jinrong Jin, Lei Yang, Yihang Xu, Le Hu, Chunchao Huang, Anqi Huang, Guangyao Qiang, Xiaogang Deng, Mingtang Xu, Ping Xu, Weixia Liu, Wanwei Zhang, Yu Deng, Yuxin Wu, Junjie Feng, Yuan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer National University of Defense Technology Changsha410073 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Computing Science College of Computer National University of Defense Technology Changsha410073 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Centre for Quantum Software and Information University of Technology Sydney Sydney2007 Australia

ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

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arXiv 2025年

作者： Shi, Zhengyuan Li, Zeju Ma, Chengyu Zhou, Yunhao Zheng, Ziyang Liu, Jiawei Pan, Hongyang Zhou, Lingfeng Li, Kezhi Zhu, Jiaying Yan, Lingwei He, Zhiqiang Xue, Chenhao Jiang, Wentao Yang, Fan Sun, Guangyu Yang, Xiaoyan Chen, Gang Shi, Chuan Chu, Zhufei Yang, Jun Xu, Qiang Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai China School of Electronic and Information Engineering Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Integrated Circuits Peking University Beijing China School of Intergrated Circuits Southeast University Nanjing China National Center of Technology Innovation for EDA Nanjing China

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 (AIGs), and placed netlists, enabling comprehensive analysis and development. We demonstrate ForgeEDA’s utility by benchmarking state-of-the-art EDA algorithms on critical tasks such as Power, Performance, and Area (PPA) optimization, highlighting its ability to expose performance gaps and drive advancements. Additionally, ForgeEDA’s scale and diversity facilitate the training of AI models for EDA tasks, demonstrating its potential to improve model performance and generalization. By addressing limitations in existing datasets, ForgeEDA aims to catalyze breakthroughs in modern IC design and support the next generation of innovations in EDA. © 2025, CC BY.

关键词： Open source software

Implementing the matrix multiplication with dfc on kunlun small scale computer 20

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Implementing the matrix multiplication with dfc on kunlun sm...

20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Du, Zheng Zhang, Jing Sha, Shihao Luo, Qiuming Department of Computer Science and Technology Tsinghua University China NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512. © 2019 IEEE.

关键词： Matrix algebra

VISION: Evaluating Scenario Suitableness for DNN Models by Mirror Synthesis

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VISION: Evaluating Scenario Suitableness for DNN Models by M...

Asia-Pacific Conference on software Engineering

作者： Ziqi Chen Huiyan Wang Chang Xu Xiaoxing Ma Chun Cao State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

software systems assisted with deep neural networks (DNNs) are gaining increasing popularities. However, one outstanding problem is to judge whether a given application scenario suits a DNN model, whose answer highly affects its concerned system's performance. Existing work indirectly addressed this problem by seeking for higher test coverage or generating adversarial inputs. One pioneering work is SynEva, which exactly addressed this problem by synthesizing mirror programs for scenario suitableness evaluation of general machine learning programs, but fell short in supporting DNN models. In this paper, we propose VISION to eValuatIng Scenario suItableness fOr DNN models, specially catered for DNN characteristics. We conducted experiments on a real-world self-driving dataset Udacity, and the results show that VISION was effective in evaluating scenario suitableness for DNN models with an accuracy of 75.6-89.0% as compared to that of SynEva, 50.0-81.8%. We also explored different meta-models in VISION, and found out that the decision tree logic learner meta-model could be the best one for balancing VISION's effectiveness and efficiency.

关键词： Neurons Mirrors Training Predictive models Support vector machines Feature extraction Biological neural networks

SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing

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arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

On Economic Viability of Mobile Edge Caching

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On Economic Viability of Mobile Edge Caching

IEEE International Conference on Communications (ICC)

作者： Changkun Jiang Lin Gao Tong Wang Jingjing Luo Fen Hou College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Key Laboratory of IoT for Smart City and the Department of Electrical and Computer Engineering University of Macau Macau China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

关键词： Internet Quality of service Pricing Economics Games Ecosystems Stakeholders

An empirical study on progressive sampling for just-in-time software defect prediction 7

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An empirical study on progressive sampling for just-in-time ...

7th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Shi, Kai Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Alibaba Group Hangzhou China

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most existing JIT-SDP work focuses on improving the prediction performance of the model by improving the model. However, a frequently ignored problem is that collecting large and high quality defect data sets is costly. Specifically, when labelling the samples, experts in the field are required to carefully analyze the defect report information and log of code modification, which requires a lot of effort. Therefore, how to build a high-performance JIT-SDP model with a small number of training samples is an issue worth studying, which can reduce the size of the defect data sets and reduce the cost of data sets acquisition. This work thus provides a first investigation of the problem by introducing a progressive sampling method. Progressive sampling is a sampling strategy that determines the minimum number of training samples while guaranteeing the performance of the model. However, progressive sampling requires that the learning curve of the prediction model be well behaved. Thus, we validate the availability of progressive sampling in the JIT-SDP issue based on six open-source projects with 227417 changes. Experimental results demonstrate that the learning curve of the prediction model is well behaved. Therefore, the progressive sampling is feasible to tackle the JITSDP problem. Further, we investigate the optimal training sample size derived by progressive sampling for six projects. Empirical results demonstrate that a high-performance prediction model can be built using only a small number of training samples. Thus, we recommend adopting progressive sampling to determine the size of training samples for the JIT-SDP problem. Copyright © 2019 for this paper by its authors.

关键词： Defects

SWRR:The Link Scheduling Algorithm Based on Weighted Round-Robin

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computers, Materials & Continua 2019年第6期59卷 965-982页

作者： Tianbo Lu Ru Yan Chao Li Lihua Yin Hao Chen Xieyu Zou School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications) Ministry of EducationBeijing100876China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Department Computer Science and Engineering The University of Texas at ArlingtonArlington76091USA

With the rapid development of the Internet,people pay more and more attention to the protection of *** second-generation onion routing system Tor is the most commonly used among anonymous communication systems,which can be used to protect user privacy *** recent years,Tor’s congestion problem has become the focus of attention,and it can affect Tor’s performance even user ***,we investigate the causes of Tor network congestion and summarize some link scheduling algorithms proposed in recent *** we propose the link scheduling algorithm SWRR based on WRR(Weighted Round Robin).In this process,we design multiple weight functions and compare the performance of these weight functions under different congestion conditions,and the appropriate weight function is selected to be used in our algorithms based on the experiment ***,we also compare the performance of SWRR with other link scheduling algorithms under different congestion conditions by experiments,and verify the effectiveness of the algorithm SWRR.

关键词： Weighted Round-Robin Tor congestion control link scheduling anonymous communication

CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark

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arXiv 2021年

作者： Yao, Yuan Dong, Qingxiu Guan, Jian Cao, Boxi Zhang, Zhengyan Xiao, Chaojun Wang, Xiaozhi Qi, Fanchao Bao, Junwei Nie, Jinran Zeng, Zheni Gu, Yuxian Zhou, Kun Huang, Xuancheng Li, Wenhao Ren, Shuhuai Lu, Jinliang Xu, Chengqiang Wang, Huadong Zeng, Guoyang Zhou, Zile Zhang, Jiajun Li, Juanzi Huang, Minlie Yan, Rui He, Xiaodong Wan, Xiaojun Zhao, Xin Sun, Xu Liu, Yang Liu, Zhiyuan Han, Xianpei Yang, Erhong Sui, Zhifang Sun, Maosong Department of Computer Science and Technology Tsinghua University China MOE Key Lab of Computational Linguistics School of EECS Peking University China Institute of Software Chinese Academy of Sciences China JD AI Research Beijing China School of Information Science Beijing Language and Culture University China School of Information Renmin University of China China National Laboratory of Pattern Recognition Institute of Automation CAS China Gaoling School of Artificial Intelligence Renmin University of China China Wangxuan Institute of Computer Technology Peking University Beijing Academy of Artificial Intelligence China

Realizing general-purpose language intelligence has been a longstanding goal for natural language processing, where standard evaluation benchmarks play a fundamental and guiding role. We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic. To this end, we propose CUGE, a Chinese Language Understanding and Generation Evaluation benchmark with the following features: (1) Hierarchical benchmark framework, where datasets are principally selected and organized with a language capability-task-dataset hierarchy. (2) Multilevel scoring strategy, where different levels of model performance are provided based on the hierarchical framework. To facilitate CUGE, we provide a public leaderboard that can be customized to support flexible model judging criteria. Evaluation results on representative pre-trained language models indicate ample room for improvement towards general-purpose language intelligence. CUGE is publicly available at ***. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems