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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Ling, Jiayao Li, Gang Hu, Qinghao Lin, Xiaolong Gu, Cheng Cheng, Jian Liang, Xiaoyao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Casia The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing China AiRiA Nanjing China

ISBN: (纸本)9783982674100

Post-Training Quantization is an effective technique for deep neural network acceleration. However, as the bit-width decreases to 4 bits and below, PTQ faces significant challenges in preserving accuracy, especially for attention-based models like LLMs. The main issue lies in considerable clipping and rounding errors induced by the limited number of quantization levels and narrow data range in conventional low-precision quantization. In this paper, we present an efficient and accurate PTQ method that targets 4 bits and below through algorithm and architecture co-design. Our key idea is to dynamically extract a small portion of significant bit terms from high-precision operands to perform low-precision multiplications under the given computational budget. Specifically, we propose Significant-Bit Quantization (SBQ). It exploits a product-aware method to dynamically identify significant terms and an error-compensated computation scheme to minimize compute errors. We present a dedicated inference engine to unleash the power of SBQ. Experiments on CNNs, ViTs, and LLMs reveal that SBQ consistently outperforms prior PTQ methods under 24-bit quantization. We also compare the proposed inference engine with state-of-the-art bit-operation-based quantization architectures TQ and Sibia. Results show that SBQ can achieve the highest area and energy efficiency. © 2025 EDAA.

关键词： Deep neural networks

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The Quality Evaluation of Academic Papers in Carbon Capture, Utilization, and Storage

The Quality Evaluation of Academic Papers in Carbon Capture,...

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2022 International Conference on Machine Learning, Control, and Robotics, MLCR 2022

作者： Zhang, Hangyue Bai, Jie School of Computer Science and Technology Sun Yat-sen University Guangzhou510275 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781665454599

Carbon capture, utilization, and storage (CCUS) technology is a research hotspot worldwide owing to global climate change and warm gas control. CCUS papers from the last twenty years, derived from the Web of science database, were analyzed to investigate the impact and citation of academic papers using machine learning methods. Firstly, nine author characteristics, keywords, and three other categories were extracted from the literature. Then, the natural logarithm distribution of the annual average number of citations was obtained, and five evaluation grades were decided based on the citations. Next, the keyword evolution of papers in the CCUS field is analyzed, and a model on the keyword evolution trend index is proposed. data integration and imbalance processing were conducted after random sampling. A citation prediction model was proposed to evaluate the quality of CCUS academic papers and was verified on the recent five years' CCUS papers by comparing it with nine other mainstream machine learning models. The results indicate that the random forest model exhibits the best performance, with an accuracy rate reaching 88.7%;an ablation experiment was also conducted using the same algorithm. The results prove the effectiveness of our proposed method, shedding light on the quality of academic papers and technology development in CCUS. © 2022 IEEE.

关键词： Climate change

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In-situ preparation of Cu-BTC modified with organic amines for H_(2)S removal under ambient conditions

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Resources Chemicals and Materials 2023年第2期2卷 143-150页

作者： Zhelin Su Mingxian Duan Yeshuang Wang Huiling Fan Zhen Tian Zhaohui Chen Chao Yang Ju Shangguan State Key Laboratory of Clean and Efficient Coal Utilization Co-founded by Shanxi Province and the Ministry of Science and TechnologyTaiyuan University of TechnologyWest Yingze Street Number 79Taiyuan030024China School of Resources and Environmental Engineering Shanghai Polytechnic UniversityShanghai 201209China State Key Laboratory of Multi-phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China College of Environmental Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China

Amine modification is an effective strategy to improve the H_(2)S removal performance of *** order to avoid the problem of pore blockage after amine modification in post synthesis impregnation,herein a series of Cu-BTC modified with organic amine adsorbents were prepared via an in-situ one-pot method and the H_(2)S removal performance under ambient conditions was *** is found that the introduced organic amines,depending on the types of amine selected,have a significant influence on the growth of Cu-BTC and its textural *** H_(2)S removal performance on the as-prepared materials suggested that the amine modified samples remarkably improved the H_(2)S removal capacities with an order of BA-Cu-BTC>TEA-Cu-BTC>TEOA-Cu-BTC>*** the enlarged surface area and the increased mesopores volumes,BA has the advantages of smaller steric hindrance and-NH_(2)groups,among which the former increased the accessibility of Cu active sites while the latter acted as additional active sites for H_(2)S capturing,thus affording BA-Cu-BTC highest breakthrough capacity of 77.3 mg S/***,this study elaborates the effect of organic amines in-situ modification on the Cu-BTC structure and desulfurization.

关键词： In-situ preparation Organic amines Cu-BTC H_(2)S adsorption

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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BMP-SD: Marrying Binary and Mixed-Precision Quantization for Efficient Stable Diffusion Inference

BMP-SD: Marrying Binary and Mixed-Precision Quantization for...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Cheng Gu Gang Li Xiaolong Lin Jiayao Ling Jian Cheng Xiaoyao Liang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China The Key Laboratory of Cognition and Decision Intelligence for Complex Systems CASIA Beijing China AiRiA Nanjing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Stable Diffusion (SD) is an emerging deep neural network (DNN) model that has demonstrated impressive capabilities in generative tasks such as text-to-image generation. However, the iterative denoising stage of the SD model is extremely expensive in both computations and memory accesses, making it challenging for fast and energy-efficient edge deployment. To alleviate the overhead of denoising, in this paper we propose BMP-SD, a post-training quantization framework for hardware-efficient SD inference. BMP-SD employs binary weight quantization to significantly reduce the computational complexity and memory footprint of iterative denoising, along with dynamic step-aware mixed-precision activation quantization, based on the observation that not all denoising steps are equally important for a specific input prompt. Experiments on the text-to-image generation task show that BMP-SD achieves mixed-precision (W1.73A4.87) with minimal accuracy loss on MS-COCO 2014 dataset. We also evaluate the BMP-SD quantized model on three state-of-the-art bit-flexible DNN accelerators, results reveal that our method can deliver up to 5.14× performance and 3.85×energy efficiency improvements compared to W8A8 quantization.

关键词： Quantization (signal) Accuracy Computational modeling Noise reduction Text to image Europe Artificial neural networks Energy efficiency Iterative methods Computational complexity

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on Software Engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： Computer languages Codes Supervised learning Natural languages Semantics Task analysis Software engineering

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Early senescence of pancreatic β cells induced by unfolded protein response deficiency prevents type 1 diabetes

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2024年第9期25卷 796-799页

作者： Haipeng CHENG Zhenwang ZHAO Dan LIU Yufei WANG Min ZHANG Department of Pathology the Second Xiangya HospitalCentral South UniversityChangsha410011China Institute of Cardiovascular Disease Key Laboratory for Arteriosclerology of Hunan ProvinceHunan International Scientific and Technological Cooperation Base of Arteriosclerotic DiseaseDepartment of Bioinformatics and Medical Big DataHengyang Medical SchoolUniversity of South ChinaHengyang421001China School of Basic Medicine Health Science CenterHubei University of Arts and ScienceXiangyang441053China Guangxi Key Laboratory of Diabetic Systems Medicine Guilin Medical UniversityGuilin541199China Medical Education Department Guangdong Provincial People’s HospitalZhuhai Hospital(Jinwan Central Hospital of Zhuhai)Zhuhai519041China

Type 1 diabetes(T1D)is a T lymphocyte-mediated autoimmune disease caused by pancreaticβ-cell destruction,which eventually leads to reduced insulin level and increased blood glucose level(Syed,2022).As a multifactorial disease,T1D is characterized by a genetic predisposition associated with various environmental and cellular elements(Syed,2022).Pancreaticβcells have long been considered the"innocent victims"in T1D pathogenesis since the pancreas is attacked by the immune cells,resulting in a process known as insulitis,in which the immune cells infiltrate pancreatic islets and secrete pro-inflammatory ***,growing evidence suggests that variousβ-cell stresses,dysfunction,and death contribute to T1D pathogenesis,as it has been observed thatβ-cell dysfunction in autoantibody-positive(Aabt)individuals exists long before T1D diagnosis(Evans-Molina et al.,2018).

关键词： diagnosis diabetes pathogenesis

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BMP-SD: Marrying Binary and Mixed-Precision Quantization for Efficient Stable Diffusion Inference

BMP-SD: Marrying Binary and Mixed-Precision Quantization for...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Gu, Cheng Li, Gang Lin, Xiaolong Ling, Jiayao Cheng, Jian Liang, Xiaoyao Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Casia The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing China AiRiA Nanjing China

ISBN: (纸本)9783982674100

关键词： Deep neural networks

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SBQ: Exploiting Significant Bits for Efficient and Accurate Post-Training DNN Quantization

SBQ: Exploiting Significant Bits for Efficient and Accurate ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jiayao Ling Gang Li Qinghao Hu Xiaolong Lin Cheng Gu Jian Cheng Xiaoyao Liang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China The Key Laboratory of Cognition and Decision Intelligence for Complex Systems CASIA Beijing China AiRiA Nanjing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Post-Training Quantization is an effective technique for deep neural network acceleration. However, as the bit-width decreases to 4 bits and below, PTQ faces significant challenges in preserving accuracy, especially for attention-based models like LLMs. The main issue lies in considerable clipping and rounding errors induced by the limited number of quantization levels and narrow data range in conventional low-precision quantization. In this paper, we present an efficient and accurate PTQ method that targets 4 bits and below through algorithm and architecture co-design. Our key idea is to dynamically extract a small portion of significant bit terms from high-precision operands to perform low-precision multiplications under the given computational budget. Specifically, we propose Significant-Bit Quantization (SBQ). It exploits a product-aware method to dynamically identify significant terms and an error-compensated computation scheme to minimize compute errors. We present a dedicated inference engine to unleash the power of SBQ. Experiments on CNNs, ViTs, and LLMs reveal that SBQ consistently outperforms prior PTQ methods under 2~4-bit quantization. We also compare the proposed inference engine with state-of-the-art bit-operation-based quantization architectures TQ and Sibia. Results show that SBQ can achieve the highest area and energy efficiency.

关键词： Quantization (signal) Accuracy Heuristic algorithms Europe Computer architecture Artificial neural networks Energy efficiency Inference algorithms Engines Faces

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Current progress,challenges,and future perspectives of language models for protein representation and protein design

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The Innovation 2023年第4期4卷 27-28页

作者： Tao Huang Yixue Li Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Guangzhou Laboratory Guangzhou 510005China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China

The sequence-structure-function paradigm of protein is the basis of molecular *** is the underlying mechanism of such sequence and structure/function corresponding relationship?We reviewed the methods for protein representation and protein *** these protein representation models,we can accurately predict many properties of proteins,such as stability and binding affinity.

关键词： representation function design.

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