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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

引用

international conference on VLSI Design

作者： Ayushparth Sharma Kusum Lata The LNM Institute of Information Technology Jaipur India

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. This article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. The proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10× higher energy-efficiency compared to serial counterpart and reduced execution period by 70 ×. Clinical validation shows that parallel architecture introduces 5-7 % error in nodule characteristic determination in comparison to serial one.

关键词： Memory management Lung cancer Very large scale integration Streaming media Energy efficiency Real-time systems parallel architectures

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Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs 26th

Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Wang, Qinglin Li, Dongsheng Huang, Xiandong Shen, Siqi Mei, Songzhu Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783030576752;9783030576745

Convolutional Neural Networks (CNNs) are widely applied in various machine learning applications and very time-consuming. Most of CNNs' execution time is consumed by convolutional layers. A common approach to implementing convolutions is the FFT-based one, which can reduce the arithmetic complexity of convolutions without losing too much precision. As the performance of ARMv8 multi-core CPUs improves, they can also be utilized to perform CNNs like Intel X86 CPUs. In this paper, we present a new parallel FFT-based convolution implementation on ARMv8 multi-core CPUs. The implementation makes efficient use of ARMv8 multi-core CPUs through a series of computation and memory optimizations. The experiment results on two ARMv8 multicore CPUs demonstrate that our new implementation gives much better performance than two existing approaches in most cases.

关键词： CNNs Convolution FFT ARMv8 parallel algorithm

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PerDL: 1st international Workshop on Deep Learning in Pervasive Computing 2021 - Welcome and Committees

PerDL: 1st International Workshop on Deep Learning in Pervas...

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IEEE Annual conference on Pervasive Computing and Communications Workshops (PerCom)

The IEEE PerDL workshop aims to address all relevant technologies, researches and discoveries in the field of distributed and pervasive Deep Learning (DL) techniques. The usage of deep learning techniques is becoming more and more widespread and needs to be properly incorporated into pervasive and distributed computing and communication architectures, in order to foster a fully connected environment of intelligent systems. The great development of Internet of Things devices and applications is fostering more and more pervasive communication paradigms, but smart devices and gateways need further capabilities to become fully connected intelligent things. What is still lacking is an in-depth study of the modalities and technologies to properly integrate all deep learning aspects and algorithms in all possible distributed and pervasive systems, making them actual intelligent distributed systems. The main aim is to support fast and on-the-field DL-based computations that may foster novel and fast services in the near future contexts, such as smart cities, smart agriculture, smart health, smart distance education, smart automotive, industry 4.0, and many more. On the other hand, DL could help in finding out better distributed and parallel configurations to increase the performance of pervasive computing itself. The objective of the PerDL workshop is to encourage the integration between the distributed and pervasive computing community and the deep artificial neural network community. This with the aim to foster the development of more and more widespread deep learning techniques in all computing and communication systems, as well as to increase pervasive computing performance by means of deep learning strategies.

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Building Agile Workflow Microservice System for HPC applications Based on Fast-start OSv

Building Agile Workflow Microservice System for HPC Applicat...

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international conference on parallel and distributed Systems (ICPADS)

作者： Qiang Hu Lixian Ma En Shao Guangming Tan State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665408790

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker.

关键词： Cloud computing conferences Buildings Microservice architectures Computer architecture Interference Containers

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Deep Learning Platforms for High-Performance and distributed Computing Object Detection 3

Deep Learning Platforms for High-Performance and Distributed...

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3rd international conference on High Technology for Sustainable Development, HiTech 2020

作者： Lazarova, Milena Nakov, Ognyan Mihaylova, Elizabet Technical University of Sofia Dept. Computer Systems Sofia Bulgaria

ISBN: (纸本)9781728186511

Deep learning developed in the last decade and has been established as recent, modern and very promising technique with large potential to be successfully applied in various domains. Despite deep learning outperforms alternative techniques in providing solution to many different problems there are several concerns and limitations of the its usage, one of them being the computational complexity of the training process. The paper discusses the possibilities of taking advantage of high-performance and distributed computing for efficiently and effectively training deep learning models for computer vision applications. A comparison of deep learning platforms and frameworks utilized on high-performance computer systems is presented and their application for computer vision problem of object detection is evaluated based on a suggested parallel computation architectural model and utilization of RetinaNet model. © 2020 IEEE.

关键词： Computer vision

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Memory Access Optimization of High-Order CFD Stencil Computations on GPU 1

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21st international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2020

作者： Wang, Shengxiang Li, Zhuoqian Che, Yonggang Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Stencils computations are a class of computations commonly found in scientific and engineering applications. They have relatively lower arithmetic intensity. Therefore, their performance is greatly affected by memory access. This paper studies the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU. Two methods are used to optimize the performance. First, we use registers to cache the data used by the stencil computations in the kernel. We use the CUDA warp shuffle functions to exchange data between neighboring grid points, and adjust the thread computation granularity to increase the data reuse. Second, we use the shared memory to buffer the grid data used by the stencil computations in the kernel, and utilize loop tiling to reduce redundant accesses to the global memory. Performance evaluation is done on an NVidia Tesla K80 GPU. The results show that compared to the original implementation that only uses the global memory, the optimized implementation that utilizes the registers achieves a maximum speedup of 2.59 and 2.79 relatively for 15M and 60M grids, and the optimized implementation that utilizes the shared memory achieves a maximum speedup of 3.51 and 3.36 relatively for 15M and 60M grids. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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A Semantic Model with Self-adaptive and Autonomous Relevant Technology for Social Media applications 25th

A Semantic Model with Self-adaptive and Autonomous Relevant ...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Samani, Zahra Najafabadi Lercher, Alexander Saurabh, Nishant Prodan, Radu Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9783030483401;9783030483395

With the rapidly increasing popularity of social media applications, decentralized control and ownership is taking more attention to preserve user's privacy. However, the lack of central control in the decentralized social network poses new issues of collaborative decision making and trust to this permission-less environment. To tackle these problems and fulfill the requirements of social media services, there is a need for intelligent mechanisms integrated to the decentralized social media that consider trust in various aspects according to the requirement of services. In this paper, we describe an adaptive microservice-based design capable of finding relevant communities and accurate decision making by extracting semantic information and applying role-stage model while preserving anonymity. We apply this information along with exploiting Pareto solutions to estimate the trust in accordance with the quality of service and various conflicting parameters, such as accuracy, timeliness, and latency.

关键词： Semantic information Community detection Pareto-trust Decentralized social media Role-stage model

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PIPArch: Programmable Image processing Architecture Using Sliding Array

PIPArch: Programmable Image Processing Architecture Using Sl...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Feiyang Wu Zhuoran Song Jing Ke Li Jiang Naifeng Jing Xiaoyao Liang Shanghai Jiao Tong University Shanghai China

Image processing arises as a promising domain for manifold applications requiring for heavy computing power and memory bandwidth with higher image resolution. Graphics processing unit (GPU) is widely used in image processing algorithms but suffers from its powerful programmability that costs high hardware overhead. Moreover, GPU consumes much energy to access data from high-capacity register files, making it hard to implement on wearable devices. Enabling low power and efficient architecture with low hardware overhead remains *** this paper, we propose a programmable image processing architecture (PIPArch) that explores the spatial locality in images to save energy while achieving high performance. We also design the instruction set architecture (ISA) to control the PIPArch. By supporting multiple parallel pipelines, we can keep the hardware utilization of PIPArch high. We evaluate the proposed PIPArch by developing the cycle-accurate simulator with some typical image processing algorithms. Compared to NVIDIA Tesla V100 GPU, PIPArch gains 23.63x speedup.

关键词： Image processing Instruction sets Wearable computers Graphics processing units Computer architecture Programmable logic arrays Hardware

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Fast GPU Implementation of Dumer’s Algorithm Solving the Syndrome Decoding Problem

Fast GPU Implementation of Dumer’s Algorithm Solving the Sy...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Shintaro Narisada Kazuhide Fukushima Shinsaku Kiyomoto KDDI Research Inc. Fujimino Japan

The syndrome decoding problem (SDP) is a security basis for code-based cryptographic algorithms such as the McEliece cryptosystem, which has been selected as a finalist for the third round of the post-quantum cryptography (PQC) standardization project organized by the National Institute of Standards and Technology (NIST). Information set decoding (ISD) is a general term for algorithms that efficiently solve SDPs based on combinatorial enumeration. In this paper, we focused on Dumer’s algorithm, which is relatively lightweight among the ISD algorithms; and proposed a multi-parallel Dumer’s algorithm optimized for computing devices that perform massively parallel processing. We implemented our algorithm on a GPU and tested it by solving some SDP instances in the decoding challenge hosted by Inria. We report that our algorithm first solved a 1161-dimensional SDP in the Goppa-McEliece setting in approximately 380 hours, which has a computational complexity of 2 52 . Our results contribute to a more detailed security analysis for code-based cryptosystems.

关键词： Performance evaluation Instruction sets Memory management Graphics processing units Standardization NIST Approximation algorithms

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Deep Learning for Surface Wave Identification in distributed Acoustic Sensing Data 8

Deep Learning for Surface Wave Identification in Distributed...

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8th IEEE international conference on Big Data (Big Data)

作者： Dumont, Vincent Tribaldos, Veronica Rodriguez Ajo-Franklin, Jonathan Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Rice Univ Houston TX USA

ISBN: (纸本)9781728162515

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130 Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170 GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

关键词： Big Data Deep Learning distributed Acoustic Sensing distributed Training Sensor Network Technology

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