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Genetic and practical swarm optimisation algorithms for patient-specific seizure detection systems 4

Genetic and practical swarm optimisation algorithms for pati...

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4th international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Ammar, Sabrina Trigui, Omar Senouci, Mohamed Ibn Khaldoun Univ Tiaret LIM Tiaret Algeria Sfax Univ ENIS ATMS Sfax Tunisia Oran Univ Ahmed Ben Bella Lab Parallel & Embedded Architectures & Intens Co Oran Algeria

ISBN: (纸本)9781538652398

the automatic seizure detection system is designed to aid the physician's decision-making process with recognizing the sought EEG segments. Increasing the system sensitivity is the goal of several studies. In fact, ameliorating this criterion allows to find the same interpretations as found with a visual scanning A patient-specific system is able to set its optimal parameters according to the patient which makes it more accurate than non patient-specific system. this paper introduces a new patient specific system with genetic and practical swarm optimisation algorithms. the results show that the proposed system is able to reach acceptable performances. Moreover, the use of the genetic algorithm improves the system sensitivity (95%) more than the practical swarm optimization (91%) which makes it a better method for the system parameter optimisation.

关键词： Epilepsy Seizures Detection Patient-Specific Seizure Detection Systems Genetic Algorithm Swarm Optimisation Algorithm

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Real-Time parallel Computing Design for Implementation of Point/Small Target Detection Algorithm in Visible/Infra-Red Video 5

Real-Time Parallel Computing Design for Implementation of Po...

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5th IEEE international conference on parallel, Distributed and Grid Computing (PDGC)

作者： Saran, Ram Kumar, Avnish Srivastava, Hari Babu Minist Def Instruments Res & Dev Estab DRDO Raipur Rd Dehra Dun 248008 India Minist Def DRDO Laser Sci & Technol Ctr LASTEC Metcalfe House Delhi 110054 India

ISBN: (纸本)9781728106465

Aerial targets such as missiles and aircrafts at far distance projected on image plane as point or small targets in infra-red and visible video. these targets lack apriori information about target dynamic, shape and size. Detection and tracking of such targets has been reported challenging task Hence, point or small size target detection algorithms become focus of long range detection and tracking systems. Generally, pre-processing is carried out on the input frame to predict the background and consequently enhance the target signature. In some scenario, post-processing algorithms are also required to reduce the false alarms. In this paper, we propose an efficient and innovative scheme for real-time implementation of point or small size target detection algorithm on PowerPC Single-Board Computer (SBC) by utilizing the parallel computing feature of AltiVec vector processing unit to achieve real-time processing. Results demonstrate the real-time processing of video with hardware results matching the simulation results.

关键词： parallel processing Long Range Target Detection Mathematical Morphology AltiVec Vector Processor Pre-processing Post-processing

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Accelerating CNN Algorithm with Fine-grained Dataflow architectures 20

Accelerating CNN Algorithm with Fine-grained Dataflow Archit...

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20th IEEE international conference on High Performance Computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Xiang, Taoran Feng, Yujing Ye, Xiaochun Tan, Xu Li, Wenming Zhu, Yatao Wu, Meng Zhang, Hao Fan, Dongrui Chinese Acad Sci ICT State Key Lab Comp Architecture Beijing Peoples R China UCAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538666142

Convolutional Neural Network(CNN) is a hot and state-of-the-art algorithm which is widely used in applications such as face recognition, intelligent monitoring, image recognition and text recognition. Because of its high computational complexity, many efficient hardware accelerators have been proposed to exploit high degree of parallel processing for CNN. However, accelerators which are implemented on FPGAs and ASICs usually sacrifice generality for higher performance and lower power consumption. Other accelerators, such as GPUs, are general enough, but they lead to higher power consumption. Fine-grained dataflow architectures, which break conventional Von Neumann architectures, show natural advantages in processing CNN-like algorithms with high computational efficiency and low power consumption. At the same time, it remains broadly applicable and adaptable. In this paper, we propose a scheme for implementing and optimizing CNN on fine-grained dataflow architecture based accelerators. the experiment results reveal that by using our scheme, the performance of AlexNet running on the dataflow accelerator is 3.11x higher than that on NVIDIA Tesla K80, and the power consumption of our hardware is 8.52x lower than that of K80.

关键词： fine-grained dataflow Convolutional Neural Network general accelerator data reuse high parallel

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High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore architectures 18

High-Performance Sparse Matrix-Matrix Products on Intel KNL ...

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47th international conference on parallel processing (ICPP) / international Workshop on Embedded Multicore Systems (EMS)

作者： Nagasaka, Yusuke Matsuoka, Satoshi Azad, Ariful Buluc, Aydin Tokyo Inst Technol Tokyo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan Lawrence Berkeley Natl Lab Berkeley CA USA Tokyo Inst Technol Dept Math & Comp Sci Tokyo Japan

ISBN: (纸本)9781450365239

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.

关键词： Sparse matrix SpGEMM Intel KNL

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Log(Graph): A Near-Optimal High-Performance Graph Representation 18

Log(Graph): A Near-Optimal High-Performance Graph Representa...

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27th IEEE/ACM/IFIP international conference on parallel architectures and Compilation Techniques (PACT)

作者： Besta, Maciej Stanojevic, Dimitri Zivic, Tijana Singh, Jagpreet Hoerold, Maurice Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450359863

Today's graphs used in domains such as machine learning or social network analysis may contain hundreds of billions of edges. Yet, they are not necessarily stored efficiently, and standard graph representations such as adjacency lists waste a significant number of bits while graph compression schemes such as WebGraph often require time-consuming decompression. To address this, we propose Log(Graph): a graph representation that combines high compression ratios with very low-overhead decompression to enable cheaper and faster graph processing. the key idea is to encode a graph so that the parts of the representation approach or match the respective storage lower bounds. We call our approach "graph logarithmization" because these bounds are usually logarithmic. Our high-performance Log(Graph) implementation based on modern bitwise operations and state-of-the-art succinct data structures achieves high compression ratios as well as performance. For example, compared to the tuned Graph Algorithm processing Benchmark Suite (GAPBS), it reduces graph sizes by 20-35% while matching GAPBS' performance or even delivering speedups due to reducing amounts of transferred data. It approaches the compression ratio of the established WebGraph compression library while enabling speedups of up to more than 2x. Log(Graph) can improve the design of various graph processing engines or libraries on single NUMA nodes as well as distributed-memory systems.

关键词： graph compression graph representation graph layout parallel graph algorithms ILP succinct data structures

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Efficient algorithms of parallel Skyline Join over Data Streams 18th

Efficient Algorithms of Parallel Skyline Join over Data Stre...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Jinchao Gu, JingZi Cheng, Shuai Li, Bo Wang, Weiping Meng, Dan Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

the issue of finding skyline tuples over multiple relations, more commonly known as the skyline join problem, has been well studied in scenarios in which the data is static. Most recently, it has become a new trend that performing skyline queries on data streams, where tuples arrive or expire in a continuous approach. A few algorithms have been proposed for computing skylines on two data streams. However, those literatures did not consider the inherent parallelism, or employ serial algorithms to solve the skyline query problem, which cannot leverage the multi-core processors. Based on this motivation, in this paper, we address the problem of parallel computing for skyline join over multiple data streams. We developed a Novel Iterative framework based on the existing work and study the inherent parallelism of the Novel Iterative framework. then we propose two parallel skyline join algorithms over sliding windows, NP-SWJ and IP-SWJ. To the best of our knowledge, this is the first paper that addresses parallel computing of skyline join over multiple data streams. Extensive experimental evaluations on real and synthetic data sets show that the algorithms proposed in this paper provide large gains over the state-of-the-art serial algorithm of skyline join over data streams.

关键词： Skyline join Sliding window Data stream parallel computing

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POWER: A parallel-Optimization-Based Framework Towards Edge Intelligent Image Recognition and a Case Study 18th

POWER: A Parallel-Optimization-Based Framework Towards Edge ...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Yang, Yingyi Mai, Xiaoming Wu, Hao Nie, Ming Wu, Hui Guangdong Power Grid Co Ltd Elect Power Res Inst Guangzhou Peoples R China

ISBN: (纸本)9783030050511;9783030050504

To improve the intelligent image recognition abilities of edge devices, a parallel-optimization-based framework called POWER is introduced in this paper. With FPGA (Field-Programmable Gate Array) as its hardware module, POWER provides well extensibility and flexible customization capability for developing intelligent firmware suitable for different types of edge devices in various scenarios. through an actual case study, we design and implement a firmware prototype following the specification of POWER and explore its performance improvement using parallel optimization. Our experimental results show that the firmware prototype we implement exhibits good performance and is applicable to substation inspection robots, which also validate the effectiveness of our POWER framework in designing edge intelligent firmware modules indirectly.

关键词： Edge intelligence Image recognition Framework parallel optimization Substation inspection robot

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GAP-8: A RISC-V SoC for AI at the Edge of the IoT 29

GAP-8: A RISC-V SoC for AI at the Edge of the IoT

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29th Annual IEEE international conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Flamand, Eric Rossi, Davide Conti, Francesco Loi, Igor Pullini, Antonio Rotenberg, Florent benini, Luca GreenWaves Technol Villard Bonnot France Univ Bologna DEI Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9781538674796

Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to process data from richer sensors such as image, video, audio, or multi-axial motion/vibration has huge application potential. However, edge processing of data-rich sensors poses the extreme challenge of squeezing the computational requirements of advanced, machine-learning-based near-sensor data analysis algorithms (such as Convolutional Neural Networks) within the mW-range power envelope of always-ON battery-powered IoT end-nodes. To address this challenge, we propose GAP-8: a multi-GOPS fully programmable RISC-V IoT-edge computing engine, featuring a 8-core cluster with CNN accelerator, coupled with an ultra-low power MCU with 30 mu W state-retentive sleep power. GAP-8 delivers up to 10 GMAC/s for CNN inference (90 MHz, 1.0V) at the energy efficiency of 600 GMAC/s/W within a worst-case power envelope of 75 mW.

关键词： Edge processing Internet of things Convolutional Neural Networks Near Sensor processing parallel architectures

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CUDA parallel programming technology application for analysis of big biomedical data based on computation of effectiveness features 5

CUDA parallel programming technology application for analysi...

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5th international conference on Information Technology and Nanotechnology, ITNT 2019

作者： Ilyasova, N. Yu Shikhevich, V.A. Shirokanev, A.S. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

this paper proposes the technology for large biomedical data analysis based on CUDA computation. the technology was used to analyze a large set of fundus images used for diabetic retinopathy automatic diagnostics. A high-performance algorithm that calculates effective textural characteristics for medical image analysis has been developed. During the automatic image diagnostics, the following classes were distinguished: thin vessels, thick vessels, exudates and a healthy area. the study of the mentioned algorithm efficiency was conducted with 500x500-1000x1000 pixels images using a square 12x12 dimension window. the acceleration relationship between the developed algorithm and various data sizes was demonstrated. the study showed that the algorithm effectiveness can be affected by certain characteristics of the image, e.g. its clarity, shape of exudate zone, variability of blood vessels, and optic disc location. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Blood vessels

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Invasive Computing for Predictability of Multiple Non-functional Properties: A Cyber-Physical System Case Study 29

Invasive Computing for Predictability of Multiple Non-functi...

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29th Annual IEEE international conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Sousa, Ericles Witterauf, Michael Brand, Marcel Tanase, Alexandru Hannig, Frank Teich, Juergen Friedrich Alexander Univ Erlangen Numberg FAU Erlangen Germany

ISBN: (纸本)9781538674796

the predictability of non-functional execution qualities is of utmost importance for the successful introduction of multi-core architectures in embedded systems requiring guarantees rather than best effort behavior. Due to the exclusive utilization of claimed resources, invasive computing provides isolation of applications on multi-core systems. this provides composability that allows to characterize and analyze individual applications statically and independent from others. In this paper, we demonstrate the principles of this resource-aware computing paradigm as an enabler for predictability of multiple non-functional properties, i.e., timing and reliability, applied to a cyber-physical system. In particular, we present the application and multi-processor implementation of a reliable and time-predictable acceleration of object detection algorithms for hard real-time control of an inverted pendulum.

关键词： Reliability Runtime Program processors Timing Uncertainty Computer architecture parallel processing"

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