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A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices

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Procedia computer Science 2020年 174卷 2-8页

作者： Zhiqiang Liu Jingfei Jiang Guoqing Lei Kai Chen Buyue Qin Xiaoqiang Zhao Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems.

关键词： Processor RISC-V Accelerator AI (Artificial Intelligence) CNN (Convolutional Neural Network) IoT (Internet of Things)

Optimal energy generation of hybrid energy systems considering economic and environmental multi-objective functions

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Science and Technology for Energy Transition (STET)

Science and technology for Energy Transition (STET) 2025年 80卷

作者： Raman, Arasu Sayed, Biju Theruvil Alkhayyat, Ahmad Ved, Amit Chetan, M. Singh, Ashish Kumar, Raj Prasad, K.D.V. Sapaev, I.B. Mohammedi, M. Department of Faculty of Business and Communications Inti International University Putra Nilai 71800 Malaysia Department of Computer Science Dhofar University PO Box 2509 Salalah211 Oman Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Centre Department of Electrical Engineering Faculty of Engineering and Technology Marwadi University Gujarat Rajkot360003 India Karnataka Bangalore India Nims School of Electrical and Electronics Engineering Nims University Rajasthan Jaipur India Chandigarh College of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali140307 India Pune India Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Western Caspian University Baku Azerbaijan Department of Electrical Engineering Islamic Azad University Branch of Central Tehran Tehran Iran

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promotin

关键词： Clean energy

Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

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arXiv 2024年

作者： Yuan, Han Hong, Chuan Jiang, Pengtao Zhao, Gangming Tran, Nguyen Tuan Anh Xu, Xinxing Yan, Yet Yen Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States College of Computer Science Nankai University China Faculty of Engineering The University of Hong Kong Hong Kong Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Radiology Changi General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template’s boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. Conclusions: In the context of pneumothorax diagnos

关键词： Diagnosis

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies

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arXiv 2022年

作者： Taylor, John Feng, Ming CSIRO Data61 CanberraACT Australia College of Engineering and Computer Science The Australian National University Canberra Australia Defence Science and Technology Group Department of Defence Canberra Australia CSIRO PerthWA Australia Centre for Southern Hemisphere Oceans Research CSIRO HobartTAS Australia

Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation regarded as a major source of interannual climate variability at the global scale. The ability to be able to make long-range forecasts of sea surface temperature anomalies, especially those associated with extreme marine heatwave events, has potentially significant economic and societal benefits. We have developed a deep learning time series prediction model (Unet-LSTM) based on more than 70 years (1950-2021) of ECMWF ERA5 monthly mean sea surface temperature and 2-metre air temperature data. The Unet-LSTM model is able to learn the underlying physics driving the temporal evolution of the 2-dimensional global sea surface temperatures. The model accurately predicts sea surface temperatures over a 24 month period with a root mean square error remaining below 0.75◦C for all predicted months. We have also investigated the ability of the model to predict sea surface temperature anomalies in the Niño3.4 region, as well as a number of marine heatwave hot spots over the past decade. Model predictions of the Niño3.4 index allow us to capture the strong 2010-11 La Niña, 2009-10 El Nino and the 2015-16 extreme El Niño up to 24 months in advance. It also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows limited skill. These results indicate the significant potential of data driven methods to yield long-range predictions of sea surface temperature *** Codes 37N10 © 2022, CC BY-NC-ND.

关键词： Atmospheric temperature

Loop Quantum Photonic Chip for Coherent Multi-Time-Step Evolution

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arXiv 2024年

作者： Zhan, Yuancheng Zhang, Hui Erbanni, Rebecca Burger, Andreas Wan, Lingxiao Jiang, Xudong Chae, Sanghoon Liu, Aiqun Poletti, Dario Kwek, Leong Chuan Nanyang Technological University Singapore Nanyang Technological University Singapore Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Institute of Intelligent Science and Technology Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai200092 China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Department of Computer Science University of Toronto Canada The Hong Kong Polytechnic University Hong Kong Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education Nanyang Technological University Singapore

Quantum evolution is crucial for the understanding of complex quantum systems. However, current implementations of time evolution on quantum photonic platforms face challenges of limited light source efficiency due to propagation loss and merely single-layer complexity. In this work, we present a loop quantum photonic chip (Loop-QPC) designed to efficiently simulate quantum dynamics over multiple time steps in a single chip. Our approach employs a recirculating loop structure to reuse computational resources and eliminate the need for multiple quantum tomography steps or chip reconfigurations. We experimentally demonstrate the dynamics of the spin-boson model on a low-loss Silicon Nitride (SiN) integrated photonic chip. The Loop-QPC achieves a three-step unitary evolution closely matching the theoretical predictions. These results establish the Loop-QPC as a promising method for efficient and scalable quantum simulation, advancing the development of quantum simulation on programmable photonic circuits. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon nitride

Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals

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arXiv 2024年

作者： Guo, Qiangbing Wu, Yun-Kun Zhang, Di Zhang, Qiuhong Guo, Guang-Can Alù, Andrea Ren, Xi-Feng Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore Singapore CAS Key Laboratory of Quantum Information CAS Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Hefei China Hefei National Laboratory University of Science and Technology of China Hefei China Photonics Initiative Advanced Science Research Center City University of New York New York United States Physics Program Graduate Center City University of New York New York United States The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics TEDA Applied Physics Institute Nankai University Tianjin China School of Chemistry and Materials Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Polarization entanglement holds significant importance for photonic quantum technologies. Recently emerging subwavelength nonlinear quantum light sources, e.g., GaP and LiNbO3 thin films, benefiting from the relaxed phase-matching constraints and volume confinement, has shown intriguing properties, such as high-dimensional hyperentanglement and robust entanglement anti-degradation. Van der Waals (vdW) NbOCl2 crystal, renowned for its superior optical nonlinearities, has emerged as one of ideal candidates for ultrathin quantum light sources [Nature 613, 53 (2023)]. However, polarization-entanglement is inaccessible in NbOCl2 crystal due to its unfavorable nonlinear susceptibility tensor. Here, by leveraging the twist-stacking degree of freedom inherently in vdW systems, we showcase the preparation of tunable polarization entanglement and quantum Bell states. Our work not only provides a new and tunable polarization-entangled vdW photon-pair source, but also introduces a new knob in engineering the entanglement state of quantum light at the nanoscale. © 2024, CC BY-NC-ND.

关键词： Van der Waals forces

Hydrodynamics and the eigenstate thermalization hypothesis

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arXiv 2024年

作者： Capizzi, Luca Wang, Jiaozi Xu, Xiansong Mazza, Leonardo Poletti, Dario Université Paris-Saclay CNRS LPTMS Orsay91405 France Department of Mathematics/Computer Science/Physics University of Osnabrück OsnabrückD-49076 Germany Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore College of Physics and Electronic Engineering Center for Computational Sciences Sichuan Normal University Chengdu610068 China Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore

The eigenstate thermalization hypothesis (ETH) describes the properties of diagonal and off-diagonal matrix elements of local operators in the eigenenergy basis. In this work, we propose a relation between (i) the singular behaviour of the off-diagonal part of ETH at small energy differences, and (ii) the smooth profile of the diagonal part of ETH as a function of the energy density. We establish this connection from the decay of the autocorrelation functions of local operators, which is constrained by the presence of local conserved quantities whose evolution is described by hydrodynamics. We corroborate our predictions through numerical simulations of two distinct nonintegrable spin-1 Ising models, exhibiting diffusive and super-diffusive transport behaviors. The simulations are performed using dynamical quantum typicality up to 18 spins, for both infinite and finite temperature regimes. Copyright © 2024, The Authors. All rights reserved.

关键词： Ising model

Effect of Depression Severity on Emotion Context Insensitivity Revealed by Facial Activities Analysis

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Effect of Depression Severity on Emotion Context Insensitivi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bochao Zou Yingxue Wang Rui Liu Xiangwen Lyu Lei Feng Huimin Ma School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China National Engineering Laboratory for Risk Perception and Prevention Beijing China National Clinical Research Centre of Mental Disorders Beijing Anding Hospital of Capital Medical University Beijing China

ISBN: (纸本)9781665429825

Major Depression Disorder (MDD) is defined as a mood condition. The Emotion Context Insensitivity (ECI) hypothesis of depression argues that the reaction sensitivity of depressed people to the changes of emotional context is attenuated. This paper designs an experiment under this hypothesis to more effectively distinguish facial cues for different levels of depression severity. By adopting publicly validated video stimuli for emotion elicitation, statistical analysis was performed to reveal significant facial features for depression severity assessment. The results found that the high severity group showed a decrease in specific action units and an increase in others, and made more intense facial movements for negative stimuli than that for positive and neutral stimuli. Hopefully, this study could benefit the interpretation of emotional functioning in depression and provide a reference for the evaluation of depression severity based on facial characteristics.

关键词： Sensitivity Statistical analysis Mood Conferences Machine learning Depression Bioinformatics