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A high performance Framework for Large-scale 2D Convolution Operation on FPGA 15

A High Performance Framework for Large-scale 2D Convolution ...

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15th International symposium on Pervasive Systems, Algorithms and Networks (I-SPAN)

作者： Zhang, Dawang Bie, Zhisong Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781538685341

Convolutional neural network (CNN), as the focus in the artificial intelligence field, has attracted more and more attention resent years. Various accelerators based on FPGA platform have been proposed because of the high performance and high energy efficiency. However, limited to the on-chip memory resources, only small networks can be accelerated by these accelerators. Due to the fact that 2D convolution operation is the most computing-intensive part, accelerating large-scale 2D convolution operation has become the key to accelerating large-scale CNNs on FPGA platforms. this paper presents a method of rotary data-storage and a design of the new PE (processing unit). Compared to the traditional Z-type PE, it performs non-redundant calculations when the stride of convolution operation is greater than 1, which significantly reduces the calculating time. At the same time, in order to reduce the external memory bandwidth with limited on-chip resources, two optimization architectures and a block calculation method have been proposed, which can reduce the usage of on-chip memory resources. As a case study, we select some convolution layers of ResNet-34 and compare it with a previous accelerator. Under the condition of same consumption of resources, the result shows that the proposed framework needs only 29.9% time and 51% external memory bandwidth of the previous accelerator.

关键词： convolution acceleration memory bandwidth FPGA

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Charm: A Language for Closed-form high-level architecture Modeling 18

Charm: A Language for Closed-form High-level Architecture Mo...

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45th ACM/IEEE Annual International symposium on computer architecture (ISCA)

作者： Cui, Weilong Ding, Yongshan Dangwal, Deeksha Holmes, Adam McMahan, Joseph Javadi-Abhari, Ali Tzimpragos, Georgios Chong, Frederic T. Sherwood, Timothy Univ Calif Santa Barbara Santa Barbara CA 93106 USA Univ Chicago Chicago IL 60637 USA IBM Res New York NY USA

ISBN: (纸本)9781538659847

As computer architecture continues to expand beyond software-agnostic microarchitecture to data center organization, reconfigurable logic, heterogeneous systems, application-specific logic, and even radically different technologies such as quantum computing, detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, cost, voltage, frequency, cooling energy, leakage, etc.) calls for a more integrative but higher-level approach. We propose Charm, a domain specific language supporting Closed-form high-level architecture Modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated and reused. the language is interpreted through a combination of symbolic evaluation (e.g., restructuring) and compiler techniques (e.g., memoization and invariant hoisting), generating executable evaluation functions and optimized analysis procedures. Further supporting reuse, a type system constrains architectural quantities and ensures models operate only in a validated domain. through two case studies, we demonstrate that Charm allows one to define high-level architecture models concisely, maximize reusability, capture unreasonable assumptions and inputs, and significantly speed-up design space exploration.

关键词： abstraction modeling DSL

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Enhanced Single Channel SSVEP Detection Method on Benchmark Dataset 15

Enhanced Single Channel SSVEP Detection Method on Benchmark ...

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15th International Conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Sozer, Abdullah Talha Karabuk Univ Elect & Elect Engn Dept Karabuk Turkey

ISBN: (纸本)9781538670330

Steady state visual evoked potential (SSVEP) is a brain response that allows a practical and high-performance brain-computer interface (BCI) to be designed. SSVEP response is a near sinusoidal waveform at a visual stimulus frequency and is time-locked to stimulus onset. this paper presents a new single channel SSVEP detection method that takes advantage of the behaviour of SSVEP response. the proposed method defines subject-specific sinusoids at the training stage. Detection of a target stimulus frequency is achieved by a correlation value between the electroencephalography (EEG) signal and subject specific sinusoids at the test stage. the performance of the developed method was compared with the well-known power spectral density analysis (PSDA) on a benchmark dataset. Experimental results show that the developed method significantly improves the SSVEP detection accuracy (by about 23%) as well as the information transfer rate (ITR) compared to PSDA methods.

关键词： Brain computer interface Steady-state visual evoked potential single channel detection

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Hybrid MPI+openMP Implementation of eXtended Discrete Element Method 30

Hybrid MPI+openMP Implementation of eXtended Discrete Elemen...

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30th International symposium on computer architecture and high performance computing, SBAC-PAD 2018

作者： Checkaraou, Abdoul Wahid Mainassara Rousset, Alban Besseron, Xavier Varrette, Sebastien Peters, Bernhard Computational Engineering Department LuXDEM Research Centre Germany Computer Science and Communications Department University of Luxembourg PCOG Luxembourg

ISBN: (纸本)9781538677698

the Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that extends classical Discrete Element Method (DEM) (which simulates the motion of granular material), by additional properties such as the chemical composition, thermodynamic state, stress/strain for each particle. It has been applied successfully to numerous industries involving the processing of granular materials such as sand, rock, wood or coke [16], [17]. In this context, computational simulation with (X)DEM has become a more and more essential tool for researchers and scientific engineers to set up and explore their experimental processes. However, increasing the size or the accuracy of a model requires the use of high performance computing (HPC) platforms over a parallelized implementation to accommodate the growing needs in terms of memory and computation time. In practice, such a parallelization is traditionally obtained using either MPI (distributed memory computing), openMP (shared memory computing) or hybrid approaches combining both of them. In this paper, we present the results of our effort to implement an openMP version of XDEM allowing hybrid MPI+openMP simulations (XDEM being already parallelized with MPI). Far from the basic openMP paradigm and recommendations (which simply summarizes by decorating the main computation loops with a set of openMP pragma), the openMP parallelization of XDEM required a fundamental code re-factoring and careful tuning in order to reach good performance. there are two main reasons for those difficulties. Firstly, XDEM is a legacy code developed for more than 10 years, initially focused on accuracy rather than performance. Secondly, the particles in a DEM simulation are highly dynamic: they can be added, deleted and interaction relations can change at any timestep of the simulation. thus this article details the multiple layers of optimization applied, such as a deep data structure profiling and reorganization, the usa

关键词： Finite difference method

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Why are repeated auctions in RaaS clouds risky? 1

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15th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2018

作者： Movsowitz, Danielle Funaro, Liran Agmon, Shunit Agmon Ben-Yehuda, Orna Dunkelman, Orr Computer Science Department University of Haifa Haifa Israel Computer Science Department Technion—Israel Institute of Technology Haifa Israel Caesarea Rothschild Institute for Interdisciplinary Applications of Computer Science University of Haifa Haifa Israel

ISBN: (数字)9783030133429

ISBN: (纸本)9783030133412

the world of cloud computing is progressing from the concept of securing resources by predefined units to dynamically allocating resources using economic mechanisms. New mechanisms offer better utilization of the hardware by sharing it among multiple users. However, they allow new types of economic attacks. We introduce two new economic attacks performed by malicious users. these attacks harm the aggregate utility of Resource-as-a-Service (RaaS) clouds. Our first attack aims at raising bills in the system, and causing victims to pay more for the same amount of resources. Over time the attack may cause victims to exhaust their budget, thus lowering their demand for resource allocation, and allowing the attacker to acquire the freed resources at a negligible cost. Our second attack is designed to hinder the victim’s performance at specific points in time by outbidding them for a single round. For resources of high regaining costs or that their full utilization takes time (e.g., RAM), even a single round without the resource may significantly hinder the performance. In this work we demonstrate on a simple representative example how the first attack reduces the victim’s profit sevenfold and the second attack causes damage of $290–$630 for every dollar spent on the attack. © 2019, Springer Nature Switzerland AG.

关键词： Resource allocation

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Identifying Cancer Biomarkers from high-throughput RNA Sequencing Data by Machine Learning 15th

Identifying Cancer Biomarkers from High-Throughput RNA Seque...

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15th International Conference on Intelligent computing, ICIC 2019

作者： Zhang, Zishuang Liu, Zhi-Ping School of Control Science and Engineering Shandong University JinanShandong250061 China

ISBN: (纸本)9783030269685

In cancer progression, the expression level of relevant genes will change significantly in tumors comparing to their healthy counterparts. therefore, the discovery of specific genes serving as biomarkers is of practical significance for diagnosis and prognosis. the available high-throughput ‘-omic’ datasets provide unprecedented resources and opportunities of deriving cancer biomarkers, such as the public RNA-sequencing data generated by the Cancer Genome Atlas (TCGA) consortium. Here, we explore the identification of biomarker genes in 12 types of cancers from the classification effects in control and disease samples by machine learning. We firstly identify differentially expressed genes individually. then, we implement feature selection by integrating recursive feature reduction and random forest classification with feature ranking. the final feature number will be determined via a parsimony principle that the features will be as few as possible, while they are still with the highest classification accuracy. In each cancer, the biomarker genes are then evaluated by tenfold cross-validations via several classification algorithms. We find extreme learning machine achieves the best classification performance when compared to the other methods. the further gene enrichment analyses indicate the dysfunctional and pathogenic mechanism in these identified biomarkers. © 2019, Springer Nature Switzerland AG.

关键词： RNA

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Human Mobility Prediction through Twitter 15

Human Mobility Prediction Through Twitter

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15th International Conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th International Conference on Future Networks and Communications (FNC)

作者： Comito, Carmela Natl Res Council Italy CNR Inst High Performance Comp & Networking ICAR Via Pietro Bucci 7-11C I-87036 Arcavacata Di Rende CS Italy

Social media, in recent years, have become an invaluable source of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. the paper presents an approach to predict human mobility by exploiting Twitter data. the prediction approach is based on a novel trajectory pattern similarity measure that allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. the pattern with the highest similarity to the user current trajectory will be used to predict the user next position. the experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the users next places achieving a remarkable precision. (C) 2018 the Authors. Published by Elsevier Ltd. this is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 13th International Conference on Future Networks and Communications, FNC-2018 and the 15th International Conference on Mobile Systems and Pervasive computing, MobiSPC 2018.

关键词： Twitter Next-place prediction Trajectory patterns Pattern similarity

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KPynq: A Work-Efficient Triangle-Inequality Based K-Means on FPGA

KPynq: A Work-Efficient Triangle-Inequality Based K-Means on...

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Annual IEEE symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Yuke Wang Zhaorui Zeng Boyuan Feng Lei Deng Yufei Ding Department of Computer Science University of California Santa Barbara Department of Electrical and Computer Engineering University of California Santa Barbara

K-means is a popular but computation-intensive algorithm for unsupervised learning. To address this issue, we present KPynq, a work-efficient triangle-inequality based K-means on FPGA for handling large-size, high-dimension datasets. KPynq leverages an algorithm-level optimization to balance the performance and computation irregularity, and a hardware architecture design to fully exploit the pipeline and parallel processing capability of various FPGAs. In the experiment, KPynq consistently outperforms the CPU-based standard K-means in terms of its speedup (up to 4.2×) and significant energy efficiency (up to 218×).

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Crash Consistency in Encrypted Non-Volatile Main Memory Systems 24

Crash Consistency in Encrypted Non-Volatile Main Memory Syst...

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24th IEEE International symposium on high performance computer architecture (HPCA)

作者： Liu, Sihang Kolli, Aasheesh Ren, Jinglei Khan, Samira Univ Virginia Charlottesville VA 22903 USA VMware Res Palo Alto CA USA Penn State Univ University Pk PA 16802 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781538636596

Non-Volatile Main Memory (NVMM) systems provide high performance by directly manipulating persistent data in memory but require crash consistency support to recover data in a consistent slate in case of a power failure or system crash. In this work, we focus on the interplay between the crash consistency mechanisms and memory encryption. Memory encryption is necessary for these systems to protect data against the attackers with physical access to the persistent main memory. As decrypting data at every memory read access can significantly degrade the performance, prior works propose to use a memory encryption technique, counter-mode encryption, that reduces the decryption overhead by performing a memory read access in parallel with the decryption process using a counter associated with each cache line. therefore, a pair of data and counter value is needed to correctly decrypt data after a system crash. We demonstrate that counter-mode encryption does not readily extend to crash consistent NVMM systems as the system will fail to recover data in a consistent state if the encrypted data and associated counter are not written hack to memory atomically, a requirement we refer to as counter-atomicity. We show that naively enforcing counter-atomicity for all NVMM writes can serialize memory accesses and results in a significant performance degradation. In order to improve the performance, we make an observation that not all writes to NVMM need to be counter-atomic. the crash consistency mechanisms rely on versioning to keep one consistent copy of data intact while manipulating another version directly in memory As the recovery process only relies on the unmodified consistent version, it is not necessary to strictly enforce counter-atomicity for the writes that do not affect data recovery. Based on this insight, we propose selective counter atomicity that allows reordering of writes to data and associated counters when the writes to persistent memory do not alter the recover

关键词： Nonvolatile memory computer crashes Encryption Memory management Hardware Optimization

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A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization 15th

A Complex-Valued Encoding Moth-Flame Optimization Algorithm ...

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15th International Conference on Intelligent computing, ICIC 2019

作者： Wang, Pengchuan Zhou, Yongquan Luo, Qifang Fan, Chencheng Xiang, Zhehong College of Information Science and Engineering Guangxi University for Nationalities Nanning530006 China Guangxi High School Key Laboratory of Complex System and Computational Intelligence Nanning530006 China

ISBN: (纸本)9783030267629

the real-valued moth-flame optimization algorithm (MFO) is a new bio-inspired algorithm. It simulates the navigation mechanism of moth lateral positioning under moonlight. MFO has excellent performance in solving optimization problems and has strong ability in solving power optimization combination. In order to improve the global search ability of the algorithm, a complex-valued encoding moth-flame optimization algorithm (CMFO) is proposed. the real and imaginary parts of the population are updated by using the diploid structure of complex-valued encoding. the diversity of the population was increased. the effectiveness of CMFO algorithm has been verified by 4 benchmark problems. Statistically significant results and analysis show that the proposed complex-valued encoding moth-flame optimization algorithm is very promising and occasionally competitive compared with other well-established meta-heuristic techniques. © 2019, Springer Nature Switzerland AG.

关键词： Global optimization

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