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SSRN 2023年

作者： Miao, Ruixuan Lu, Xu Tian, Cong Yu, Bin Cui, Jin Duan, Zhenhua Institute of Computing Theory and Technology State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science Xi’an Shiyou University China

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTLf (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTLf into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTLf specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Evaluating and Constraining Hardware Assertions with Absent Scenarios

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Journal of computer Science & technology 2020年第5期35卷 1198-1216页

作者： Hui-Na Chao Hua-Wei Li Xiaoyu Song Tian-Cheng Wang Xiao-Wei Li State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518052China Department of Electrical and Computing Engineering Portland State UniversityPortlandOR 97207U.S.A.

Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion *** the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined *** paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated *** considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower *** Belief-failRate guided assertion constraining method leverages the quality of generated *** experimental results show that the Belief-failRate framework performs better than the existing *** addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.

关键词： hardware formal verification assertion generation data mining assertion evaluation assertion coverage

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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ASBP: Automatic Structured Bit-Pruning for RRAM-Based NN Accelerator 21

ASBP: Automatic Structured Bit-Pruning for RRAM-Based NN Acc...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Songyun Qu Bing Li Ying Wang Lei Zhang Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences Capital Normal University Institute of Computing Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781665432740

Network sparsity or pruning is an extensively studied method to optimize the computation efficiency of deep neural networks (DNNs) for CMOS-based accelerators, such as FPGAs and GPUs. Though the RRAM-based accelerator has demonstrated superior performance and energy efficiency for DNN tasks, deploying the sparse neural networks desires dedicated consideration to save resource consumption without introducing the expensive index overhead and sophisticated control. To exploit the potential of sparse neural network design on the RRAM-based accelerator, we propose an automatic structured bit-pruning design, ASBP, to harmonize the optimization objective of DNN sparsity with efficient RRAM deployment. Specifically, ASBP prunes the bits of weight which are split into different crossbars and thus, free the zero-value crossbar when mapping the neural network into RRAM-based accelerators without extra hardware modification. Meanwhile, ASBP employs the reinforcement learning (RL) approach to automatically select the best crossbar-aware bit-sparsity strategy for any given neural network without laborious human efforts. According to our experiments on a set of representative neural networks, ASBP saves up to 79.01% energy consumption and 54.79% area overhead compared to the baseline that deploys the original DNN on the RRAM-based accelerator. Besides, ASBP outperforms the state-of-the-art bit-sparsity design by 1.4x in terms of the energy reduction on the RRAM-based accelerator.

关键词： RRAM

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FPGA-Based Multi-precision architecture for Accelerating Large-Scale Floating-Point Matrix computing 17th

FPGA-Based Multi-precision Architecture for Accelerating Lar...

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17th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2020

作者： Zhang, Longlong Peng, Yuanxi Hu, Xiao Huang, Ahui Tian, Tian State Key Laboratory of High Performance Computing School of Computer National University of Defense Technology Changsha China Institute of Microelectronics School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030794774

Matrix computing plays a vital role in many scientific and engineering applications, but previous work can only handle the data with specified precision based on FPGA. This study first presents algorithms, data flows, and mapping strategies to match the hardware structure for matrix computing of different precisions. Then, we propose a unified multi-precision matrix computing unit core that can handle three precisions and three matrix operation modes and can be used as a coprocessor for large-scale matrix computing which has advantages of low storage and high efficiency. Finally, we build a complete matrix computing acceleration system and deploy it on FPGA using 128 processing elements (PEs). The experimental results show that the accelerator achieves a maximum frequency of 180 MHz, and matrix computing of double-precision, single-precision, and half-precision floating-point data performs 46.1 GFLOPS, 92.1 GFLOPS, and 184.3 GFLOPS respectively, which is superior to other current designs in terms of application range and performance. © 2021, IFIP International Federation for Information Processing.

关键词： Field programmable gate arrays (FPGA)

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Evolving Physical Instinct for Morphology and Control Co-Adaption

Evolving Physical Instinct for Morphology and Control Co-Ada...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xinglin Chen Da Huang Minglong Li Yishuai Cai Zhuoer Wen Zhongxuan Cai Wenjing Yang Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China School of Computer science Peking University Beijing China

The capability of a robot to perform tasks depends not only on precise motion control, but also on a well-suited body morphology. Adapting both morphology and control of robots to improve their task performance has been a widely studied and long-standing issue. While the bio-inspired bi-level optimization framework has gained popularity in recent years, it suffers from high computation complexity due to the time-consuming and inefficient learning process for each morphology. In fact, in nature, besides the adaptive morphology and the intelligent brain, animals also possess an important gift, which is physical instinct. These instincts allow animals to respond quickly to their surroundings in the neonatal period, facilitating skills acquisition. Inspired by this, we propose an evolvable instinct controller to enhance the morphology-control co-adaption. The instinct controller suggests rough motion inclinations, which require minimal domain knowledge and entail less sophisticated design. Its purpose is to assist the main controller in learning fine-grained and robust control efficiently. We implemented this idea in the context of legged locomotion and designed the instinct controller using phase-based FSMs. We propose the instinct-based co-adaption algorithm and construct GPU parallel simulation experiments on different morphology prototypes. The results indicate that combining the co-adaption process with instinct evolution leads to the development of superior morphologies and robust controllers compared with the conventional co-adaption approach, with minimal additional time cost.

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A Cross-domain Radar Emitter Recognition Method with Few-shot Learning

A Cross-domain Radar Emitter Recognition Method with Few-sho...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Yixian Luo Tianrui Liu Xueqiong Li Hengzhu Liu Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

In many realistic scenarios, it is necessary but challenging to acquire a large number of annotated radar emitter samples for training a recognition model. This study proposes a cross-domain radar emitter recognition method with few-shot learning, which introduces model-agnostic meta-learning (MAML) to recognize radar emitter and improves it to adapt to various types of radar emitter in different domains without spending a lot of time and data to retrain the model. This method can learn an ideal initialization parameter with a few source domain samples, and then initialize with this parameter on a completely different target domain. It can obtain good generalization effect by fine-tuning with fewer samples, so as to realize cross-domain recognition of radar emitter. Simulation results show that the accuracy of the proposed method can reach more than 90% in high-noise target domains with completely different data distribution from the source domain, which shows its superiority in recognition performance and sample size compared with other radar emitter recognition methods.

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MRDRL-ROS: A Multi Robot Deep Reinforcement Learning Platform based on Robot Operating System 4

MRDRL-ROS: A Multi Robot Deep Reinforcement Learning Platfor...

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2021 4th International Conference on Mechatronics and computer technology Engineering, MCTE 2021

作者： Cai, Zhongxuan Liang, Zhen Ren, Jing Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Hunan Changsha410073 China

Deep reinforcement learning (DRL) has greatly improved the intelligence of AI in recent years and the community has proposed several common software to facilitate the development of DRL. However, in robotics the utility of common DRL software is limited and the development is time-consuming due to the complexity of various robot software. In this paper, we propose a software engineering approach leveraging modularity to facilitate robot DRL development. The platform decouples learning environment into task, simulator and hierarchical robot modules, which in turn enables diverse environment generation using existing modules as building blocks, regardless of the underlying robot software details. Experimental results show that our platform provides composable environment building, introduces high module reuse and efficiently facilitates robot DRL. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Reinforcement learning

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection 3rd

A Benchmark of Ocular Disease Intelligent Recognition: One S...

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3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Li, Ning Li, Tao Hu, Chunyu Wang, Kai Kang, Hong College of Computer Science Nankai university Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing100190 China Beijing Shanggong Medical Technology Co. Ltd. Beijing100176 China

ISBN: (纸本)9783030710576

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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