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Data Locality Aware Computation Offloading in Near Memory processing Architecture for Big Data applications

Data Locality Aware Computation Offloading in Near Memory Pr...

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international conference on High Performance Computing

作者： Satanu Maity Mayank Goel Manojit Ghose Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

The data-intensive applications of today's big data era often produce a large memory footprint. As a result, a significant volume of data needs to travel from memory to the CPU under the traditional Von-Neumann computing paradigm. Near-memory processing (NMP) or processing-in-memory (PIM) is a potential alternate computation framework where a computation unit is placed near the memory (or inside the memory) and a portion of an application is executed on it (termed computation offloading) aiming to reduce the amount of data movement and its consequences. Although a few computation offloading strategies have been proposed in recent times, the existing approaches do not consider the data locality offered by the last level cache and the overall execution time of the application while designing their policies. In this paper, we propose a data locality-aware computation offloading strategy for a hybrid computing system comprising the host processor and NMP-enabled 3D memory. After the application code is instrumented using the LLVM compiler framework, the strategy offloads a portion of an application to NMP if its estimated overall execution time is less. An extensive simulation performed on a set of standard simulators for a bunch of large graph-based application benchmarks reports the effectiveness of the proposed strategy by achieving a maximum speedup of 40% and 11.8% as compared to the host-only configuration and the state-of-art policy, respectively. The proposed strategy also reduces the off-chip data transfer and energy consumption by a significant margin as compared to the host-only configuration (avg 27%) and the state-of-art policy (avg 28%). Further, the proposed policy reduces the LLC miss rate by 57% as compared to the state-of-art policy.

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A Cost-Effective Approach For Real-Time Anemia Diagnosis Using An Automated image processing Tool Interfaced Paper Sensor

A Cost-Effective Approach For Real-Time Anemia Diagnosis Usi...

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ieee India conference (INDICON)

作者： Smriti Sinha Akashlina Basu Shirin Dasgupta Soumen Das School of Medical Science and Technology Indian Institute of Technology Kharagpur India Dr. B.C.Roy Multispeciality Medical Research Centre Indian Institute of Technology Kharagpur India

A large proportion of the world’s population from low and middle-income countries, particularly women of reproductive age and young children, are vulnerable to anemia. Anemia reduces work productivity and increases mortality in chronic ailments such as kidney, cardiovascular, acquired immunodeficiency diseases, etc. Therefore, mass screening of anemic population and early treatment are crucial for public health programs. While various POC device has been reported earlier for rapid, affordable sensing of anemia, these methods have several limitations that hinder their broad implementation in practical applications. Here, we report an on-card approach for anemia detection by measuring the blood-spreading area on a cellulose paper platform. The image analysis was coupled with a Python-based image processing tool for the automated measurement of blood Hb. This device was standardized using a minimal blood volume of 10μl and demonstrates a sensitivity of -5.17 ± 0.11 (mm 2 /(g/dl)) and can efficiently measure Hb as low as 1.07g/dl. On comparison with the gold standard method, strong correlation(r=0.99) and good accuracy (mean bias= 0.15g/dl with a narrow limit of agreements, i.e., -0.67 to 0.98 g/dl) was achieved. This device operates reagent-free and thus is not susceptible to interference caused by storing and transportation. Automated analysis precluded the chance of error due to manual handling, thus reducing the risk of misdiagnosis. The developed paper sensor offers a simple, affordable, automated method for real-time anemia detection and potentially facilitates anemia screening in poor settings.

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Feature Extraction from image Sensing (Remote) : image Segmentation

Feature Extraction from Image Sensing (Remote) : Image Segme...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Kaushal Chauhan Harshit Tomar Kushagra Kamal Pallavi Goel Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

In this research paper, an overview of computer methods for segmenting continuous-tone images into meaningful parts and characterizing these parts with “features” is presented. image segmentation is an essential pre-processing step in computer vision and image processing softwares. This paper discusses various techniques and algorithms used for image segmentation, including both supervised and unsupervised methods. The performance of these techniques is evaluated on different test images, and future research directions are proposed. Feature extraction from image sensing is a critical step in multimedia processing. This paper explores the process of feature extraction from remotely sensed images. With the increasing use of remote sensing technology, it is crucial to have effective and accurate methods for extracting features from remotely sensed data, which are utilized in various applications such as land-use mapping, environmental proctoring, and in natural-disaster management. This paper discusses several techniques for feature extraction, including pixel-based, object-based, and machine learning-based approaches. Additionally, it highlights the difficulties associated with feature extraction from remote sensing data, such as atmospheric effects, radiometric and geometric distortions, and the presence of noise. The paper concludes by discussing future directions in remote sensing feature extraction, including the integration of deep learning methods and the development of more robust methods has the ability of handling large-scale datasets. With the help of deep learning methods and algorithms, especially CNN’s, has tremendously improved the accuracy of image scene classification. In this study, a CNN model which is already trained was utilized to fetch-out deep features from images at various network layers, including the average pooling layer.

关键词： Deep learning image segmentation Scene classification Noise Neural networks Feature extraction Radiometry

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Single Photon Detectors for Quantum Computing

Single Photon Detectors for Quantum Computing

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Proceedings of the ieee Dallas Circuits and Systems Workshop (DCAS)

作者： Mst Shamim Ara Shawkat Sajid Hasan Nicole McFarlane Department of Electrical and Computer Engineering Florida International University Florida USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee USA

Optical quantum entanglement has emerged as a promising tool to solve many previously intractable problems in science and finds applications in signal processing, communication, and encryption. Quantum systems based on photons require optical detectors which are able to detect single photons, have a high photon to electron conversion gain, linear, low noise, and have a wide detection range. In order to be competitive with traditional computing systems in terms of size, weight, and power and to facilitate wider adoption the detectors should be low cost, scalable, and facilitate integration with the backend electronics. For these applications, single photon avalanche diodes (SPADs) are becoming more popular. They can be implemented in scalable formats using commercial silicon foundries where the cost per unit is low. However, these silicon SPADs suffer from higher noise and lower photon detection efficiency than other semiconducting materials. This paper we review an improved version of SPAD single photon detectors, perimeter gated SPAD (PGSPAD), which show improved noise performance and are suitable for quantum computing.

关键词： Photomultipliers Quantum computing Costs Detectors Logic gates Optical detectors Silicon

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Effective Whole-body Pose Estimation with Two-stages Distillation

Effective Whole-body Pose Estimation with Two-stages Distill...

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international conference on computer Vision Workshops (ICCV Workshops)

作者： Zhendong Yang Ailing Zeng Chun Yuan Yu Li Tsinghua Shenzhen International Graduate School International Digital Economy Academy (IDEA)

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an image. This task is challenging due to multi-scale body parts, fine-grained localization for low-resolution regions, and data scarcity. Meanwhile, applying a highly efficient and accurate pose estimator to widely human-centric understanding and generation tasks is urgent. In this work, we present a two-stage pose Distillation for Whole-body Pose estimators, named DWPose, to improve their effectiveness and efficiency. The first-stage distillation designs a weight-decay strategy while utilizing a teacher’s intermediate feature and final logits with both visible and invisible keypoints to supervise the student from scratch. The second stage distills the student model itself to further improve performance. Different from the previous self-knowledge distillation, this stage finetunes the student’s head with only 20% training time as a plug-and-play training strategy. For data limitations, we explore the UBody dataset that contains diverse facial expressions and hand gestures for real-life applications. Comprehensive experiments show the superiority of our proposed simple yet effective methods. We achieve new state-of-the-art performance on COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from 64.8% to 66.5%, even surpassing RTMPose-x teacher with 65.3% AP. We release a series of models with different sizes, from tiny to large, for satisfying various downstream tasks. Our code and models are available at https://***/IDEA-Research/DWPose.

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22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learning-Based Visual Object processing Unit Supporting Multi-Scale Semantic Feature Extraction for Mobile Object Detection/Tracking applications

22.7 DL-VOPU: An Energy-Efficient Domain-Specific Deep-Learn...

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ieee international conference on Solid-State Circuits (ISSCC)

作者： Yuchuan Gong Teng Zhang Hongtao Guo Xiyuan Liu Jingxiao Zheng Hongqiang Wu Conghan Jia Luying Que Liang Zhou Liang Chang Jun Zhou University of Electronic Science and Technology of China Chengdu China

In the recent years, deep learning-based visual object detection/tracking (VODT) has been widely used in intelligent applications such as autonomous driving, UAV, smart robot and $\text{VR}/\text{AR}$ . As general Al hardware platforms, GPUs and general Al processors are often used for accelerating VODT. However, without a domain-specific architecture, it is difficult for these processors to achieve high energy efficiency, making them unsuitable for mobile VODT applications. Recently, some dedicated VODT processors have been proposed with improved energy efficiency [1]–[3]. As shown in $\text{Fig}. 22.7.1$ , these designs have several issues: 1) they only support a single task (either detection or tracking), 2) they lack full support for multi-scale semantic feature extraction $(\text{MSFE}){-}$ based state-of-the-art VODT frameworks [4], and 3) they do not sufficiently exploit domain-specific features for energy efficiency optimization. To address these issues, in this work, a deep learning-based visual object processor (named DL-VOPU) is proposed for mobile VODT applications. It exploits diverse domain-specific features to achieve record-high energy efficiency for VODT, while supporting MSFE-based VODT frameworks with a programmable backbone network. The DL-VOPU features: 1) an energy-efficient MSFE-aware Al architecture, 2) an object-oriented adaptive computing technique for energy-efficient object tracking, 3) a parallel frame-difference computing technique for energy-efficient neural network (NN) computation on video streams, and 4) a unified data compression & computing technique to address data redundancy in VODT processing.

关键词： Visualization Program processors Semantics Redundancy computer architecture Streaming media Feature extraction

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Research on Feature-based image Recognition based on Convolutional Neural Network

Research on Feature-based Image Recognition based on Convolu...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee international conference on

作者： Qi Ma Zeyu Yang Qingyao Zhang Yejia Yang Haibin Ran Yuhao Bian College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Recently, image identification has achieved promising performance and become the most essential component in real-life image-based applications through training a machine learning model. However, existing methods extract the features and the identification accuracy is almost depended on these features with extractions process, which is the fundamental process for learning models. Existing methods are almost concentrated on the utilization of these feature and ignore the process of extracting procedure. As far as we can concern, the image features contain numerous information and can assist the learning model to enhance the identification accuracy. In this work, we initially utilize a convolutional neural network to obtain the features in the feature space. Subsequently, an attention mechanism and the feature clustering are applying to optimize the extracted features. Finally, we evaluate our proposed model with most used methods in the identical environment setups. From our extensive experimental results, we can observe that our model outperforms compared methods with reasonable computation costs.

关键词： Training Adaptation models Computational modeling Scalability Transfer learning Process control Feature extraction

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A Robust Estimation Method for Automatic Registration of Remote Sensing imagery

A Robust Estimation Method for Automatic Registration of Rem...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), international conference on

作者： Dhanya S Pankaj Rama Rao Nidamanuri Dept. of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram India Dept. of Earth and Space Sciences Indian Institute of Space Science and Technology Thiruvananthapuram India

image registration is a fundamental process in the processing and analysis of remote sensing imagery. Given the enormous increase in the availability and variety of modern remote sensors, automated registration of satellite images has become an important pre-processing task. A wide range of registration techniques has been developed for various types of applications and data. The three main stages involved in image registration are the generation of matching points, estimation of mapping functions, and resampling. The classic robust estimator, random sample consensus (RANSAC) algorithm has been the method of choice to accomplish this task. However, the speed of convergence of RANSAC depends on a number of factors like percentage of inliers, complexity of the transformation model etc. and there have been a few improved versions of RANSAC in literature. The ability to deduce an accurate transformation model in the presence of outliers is crucial to achieve accurate image registration results. This paper presents a new robust estimator, ProLoSAC, which performs filtering of matching points to estimate the mapping function accurately while maintaining the speed of RANSAC algorithm for image registration. We have applied the proposed algorithm for the automatic registration of multi-sensor multi-resolution remote sensing images namely, Resoucesat-1 AWIFS, GeoEye-1, Landsat-8, Pleiades-1A and AISA hyperspectral images. The validation of results carried out using various error metrics indicates that the proposed algorithm is superior to the existing approach for satellite image registration.

关键词： Point cloud compression image registration Satellites Three-dimensional displays Estimation Registers Task analysis

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Fast Full-frame Video Stabilization with Iterative Optimization

Fast Full-frame Video Stabilization with Iterative Optimizat...

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international conference on computer Vision (ICCV)

作者： Weiyue Zhao Xin Li Zhan Peng Xianrui Luo Xinyi Ye Hao Lu Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Albany Albany NY

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multi-frame fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.

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Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence 3rd

Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

作者： Li, Shuai Chen, Jinlong Yang, Minghao Su, Jianhua Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guangxi Guilin541004 China Beijing100190 China HuaiRou Beijing101400 China Beijing100190 China

ISBN: (纸本)9789819721993

Object 6D pose estimation is essential for a variety of applications, including autonomous driving, robotic manipulation, and automated harvesting. Due to the influence of different lighting conditions and occlusions, objects with different poses may exhibit significant variations in appearance in different views. This makes the estimation of the 6D pose of objects from a single RGB image a challenging task. This paper proposes a deep learning network capable of simultaneously detecting and estimating the 6D pose (3D rotation + 3D translation) of target objects. As establishing a large-scale dataset using real data is extremely challenging, this paper introduces an automatic collection scheme based on virtual data generated from real images. We conducted experiments on the generated astronaut dataset using our data collection scheme for 6D pose estimation. The experimental results show that the accuracy of 6D pose estimation based on the ADD metric is 96%. Therefore, this method has significant potential and can be extended to other downstream tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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