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Pipeline Architecture for N==K*2L Bit Modular ALU: Case Study between Current Generation Computing and Vedic Computing

Pipeline Architecture for N==K*2L Bit Modular ALU: Case Stud...

引用

international conference for Convergence of Technology (I2CT)

作者： G.N. Chiranjeevi Subhash Kulkarni Department of Electronics and Communication Engineering PESIT-Bangalore South Campus Bangalore Affiliated to Visvesvaraya Technological University Belagavi Karnataka India

ISBN: (纸本)9781728188768;9781728188775

this paper describes a design architecture that performs mathematical operations using Vedic sutra for upward compatibility in pipeline manner. In spite of increasing the area, performance and reducing power, Vedic architecture have observed to be inherently compatible with higher efficiency for pipeline architecture. However Vedic architecture leads to additional flexibility starting from 2-bit base modules to 8-bit modules (L=1,2,3) and pipeline can be compactable to any base modules for any given length. Many researchers proposed arithmetic algorithms at simulation level using vedic sutra. these algorithm have been evaluated with better performance, area and speed. the literature has been widely found to be towards individual arithmetical operators like multiplier, square and cube and so on. A consolidated computing architecture, especially N-bit ALU is yet be realized Generalized N-bit ALU, can always be realized using pipeline modular architecture. the proposition is on realizing N-bit using `2^L, as base modules using `K' modules in pipelining. the authors have extensively verified modular architecture for 4-Bit modules for 16 bit and 32 bit pipelined operations. Individually multiplication using Urdhva Tiryakbhyam, division using Dhwajanka sutra, square using Dwandwayoga sutra. MAC unit which involves multiplication algorithms used in FFT and IFFT using sutras of Vedic mathematics and it is possible to achieve reduce version interms of speed and delay, compared to different generations of ALU the authors are now exploring N-bit ALU architecture FPGA implementation using Vedic sutras with flexible modular pipeline architecture and mainly targeted for Digital Signal processing applications.

关键词： Computational modeling Signal processing algorithms Process control Computer architecture Digital signal processing parallel architectures Delays

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clique: A parallel Tool for the Molecular Nanomagnets Simulation and Modelling 13th

clique: A Parallel Tool for the Molecular Nanomagnets Simula...

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13th international conference on parallel processing and Applied Mathematics, PPAM 2019

作者： Antkowiak, Michal Kucharski, Lukasz Haglauer, Monika Faculty of Physics Adam Mickiewicz University ul. Uniwersytetu Poznańskiego 2 Poznań61-614 Poland

ISBN: (纸本)9783030432218

A powerful program for modelling the molecular nanomagnets is presented. the exact diagonalization method is used, which gives numerically accurate results. Its main bottleneck is the diagonalization time of large matrices, however it is removed by exploiting the symmetry of the compounds and implementing the method in the parallel computing environment. the diagonalization scheduling algorithm is implemented to increase the balance of the parallel processes workload. the running times of two different diagonalization procedures are compared. © 2020, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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An Efficient New Static Scheduling Heuristic for Accelerated architectures 20th

An Efficient New Static Scheduling Heuristic for Accelerated...

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20th Annual international conference on Computational Science (ICCS)

作者： McSweeney, thomas Walton, Neil Zounon, Mawussi Univ Manchester Manchester Lancs England Numer Algorithms Grp NAG Manchester Lancs England

ISBN: (纸本)9783030503710;9783030503703

Heterogeneous architectures that use Graphics processing Units (GPUs) for general computations, in addition to multicore CPUs, are increasingly common in high-performance computing. However many of the existing methods for scheduling precedence-constrained tasks on such platforms were intended for more diversely heterogeneous clusters, such as the classic Heterogeneous Earliest Finish Time (HEFT) heuristic. We propose a new static scheduling heuristic called Heterogeneous Optimistic Finish Time (HOFT) which exploits the binary heterogeneity of accelerated platforms. through extensive experimentation with custom software for simulating task scheduling problems on user-defined CPU-GPU platforms, we show that HOFT can obtain schedules at least 5% shorter than HEFT's for medium-to-large numerical linear algebra application task graphs and around 3% shorter on average for a large collection of randomly-generated graphs.

关键词： High-Performance Computing GPU computing Scheduling Precedence constraints Directed Acyclic Graphs

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Deep learning for social network information cascade analysis: A survey 5

Deep learning for social network information cascade analysi...

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5th IEEE international conference on Data Science in Cyberspace, DSC 2020

作者： Gao, Liqun Zhou, Bin Jia, Yan Tu, Hongkui Wang, Ye Chen, Chenguang Wang, Haiyang Zhuang, Hongwu National University of Defense Technology College of Computer ChangSha China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

the phenomenon of information dissemination in social networks is widespread, and Social Network Information Cascade Analysis (SNICA) aims to acquire valuable knowledge in the process of information dissemination in social networks. As the number, volume, and resolution of social network data increase rapidly, traditional social network data analysis methods, especially the analysis method of social network graph (SNG) data becoming overwhelmed in SNICA. Recently, deep learning models have changed this situation, and it has achieved success in SNICA with its powerful implicit feature extraction capabilities. In this paper, we provide a comprehensive survey of recent progress in applying deep learning techniques for SNICA. We first introduce related concepts and summarize the advantages of deep learning technology in SNICA. then, we propose a general framework for deep learning technology, which applies to SNICA. then, different SNICA application scenarios in the framework are classified and discussed according to user behavior analysis, information cascade analysis, rumor detection, and social network event analysis. Finally, we discuss the limitations of current work and suggest future directions. © 2020 IEEE.

关键词： Data mining

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th international conference on Video and Image processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. the additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. the experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Robust Deep Representation Learning for Road Crack Detection 21

Robust Deep Representation Learning for Road Crack Detection

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Proceedings of the 2021 5th international conference on Video and Image processing

作者： Shadrack Fred Mahenge Stephen Wambura Licheng Jiao School of Artificial Intelligence Xidian University China Computer Studies Dar es Salaam Institute of Technology Tanzania

ISBN: (纸本)9781450385893

Computer vision (CV) based inspection has recently attracted considerable attention and is progressively replacing traditional visual inspection which is subject to poor accuracy, high subjectivity and inefficiency. this paper, benefiting from hybrid structures of multichannel parallel convolutional neural networks (pCNNs), introduces a unique deep learning framework for road crack detection. Ideally, CNN-based frameworks require a relatively huge computing resources for accurate image analysis. However, the portability objective of this work necessitates the utilization of low power processing units. To that purpose, we propose robust deep representation learning for RoadCrackDetection (RoCDe) which uses a multichannel pCNNs. Bayesian optimization algorithm (BOA) was used to optimize the multichannel pCNNs training with the fewest possible neural network (NN) layers to achieve the maximum accuracy, improved efficiency and minimum processing time. the CV training was done using two distinct optimizers namely Adam and RELU on a sufficiently available datasets through image preprocessing and data augmentation. Experimental results show that, the proposed algorithm can achieve high accuracy around 95% in crack detection, which is good enough to replace human inspections normally conducted on-site. this is largely due to well-calibrated predictive uncertainty estimates (WPUE). Effectiveness of the proposed model is demonstrated and validated empirically via extensive experiments and rigorous evaluation on large scale real world datasets. Furthermore, the performance of hybrid CNNs is compared with state-of-the art NN models and the results provides remarkable difference in success level, proving the strength of multichannel pCNNs.

关键词： Image processing deep learning machine learning computer vision object detection

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A knowledge graph embedding method based on neural network 5

A knowledge graph embedding method based on neural network

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5th IEEE international conference on Data Science in Cyberspace, DSC 2020

作者： Li, Chenchen Li, Aiping Tu, Hongkui Wang, Ye Wang, Changhai National University of Defense Technology College of Computer ChangSha China National University of Defense Technology College of Computer Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the feature embedding of entities and relations without considering contextual information. therefore, in this paper, we propose an effective KGE model based on neural network. First of all, we convert the triple (h, r, t) of the KG into a sentence [h r t]. then, the LSTM neural network is used to learn the long-term dependence of sentences from the input feature vectors. then, on this basis, the two-layer convolutional neural network with several different filters is used to extract different local features. Finally, the obtained feature vectors are connected together, and the inner product is carried out with the weight vectors to obtain the score of the triple, so as to judge the validity of the given triple. We evaluate our model on two benchmark datasets FB15k-237 and WN18RR, the experimental results show that the model can effectively improve the accuracy of link prediction, achieving better results compared with other baseline models. © 2020 IEEE.

关键词： Knowledge graph

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the Research of parallel Wavelet Transform algorithms on Remote Sensing Image

The Research of Parallel Wavelet Transform Algorithms on Rem...

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international conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Shengzhong Zhang Information Network Center China University of Geosciences (Beijing) Beijing China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199290

Focusing on the Wavelet Transform, the paper explores four parallel Wavelet Transform algorithms and techniques from the perspectives of data parallel and algorithm parallel for remote sensing images. Among them, the algorithm based on "Working Pool parallel" achieves dynamic load balance without any limits to the scale of the data and the number of the Slaves. therefore, this algorithm is easier to achieve the goal of processing the vast data of remote sensing images rapidly in the distributed network systems.

关键词： Wavelet transforms Filtering Task analysis Heuristic algorithms Recycling Wavelet analysis parallel algorithms

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parallel Stateful Logic in RRAM: theoretical Analysis and Arithmetic Design 30

Parallel Stateful Logic in RRAM: Theoretical Analysis and Ar...

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30th IEEE international conference on Application-Specific Systems, architectures and Processors (ASAP)

作者： Wang, Feng Luo, Guojie Sun, Guangyu Zhang, Jiaxi Huang, Peng Kang, Jinfeng Peking Univ Ctr Energy Efficient Comp & Applicat Beijing Peoples R China Peking Univ Inst Microelect Beijing Peoples R China

ISBN: (纸本)9781728116013

processing-in-memory (PIM) provides massive parallelism with high energy efficiency and becomes a promising solution to the "memory wall" problem. Recently, the emerging metal-oxide resistive random access memory (RRAM) has shown its potential to design a PIM architecture. Several stateful logic operations, e.g., NOR and NAND, can be executed in parallel in an RRAM crossbar. Although previous works have designed some algorithms using the stateful logic, it is still under exploration how to fully exploit its potential high parallelism and design an asymptotically fast algorithm for a given function. In this work, we theoretically analyze the parallelism in an RRAM crossbar and design several asymptotically optimal arithmetic algorithms. In detail, we first propose the Single Instruction Multiple Lines (SIML) model to unify the stateful logic families and prove three lower bounds on the time complexity of a parallel RRAM algorithm. then, we design three algorithms for integer addition functions with the stateful logic, guided by the lower bound analysis. All of them reach the time complexity lower bound. Finally, We make two extensions of the integer addition algorithms, supporting multiplication functions by decomposing them to additions and supporting the flex-point data type by proposing an exponent and mantissa update flow. Experimental evaluation shows that our integer algorithms achieves a speedup up to 13.79x over the previous RRAM algorithms. Our flex-point implementation achieves a 26.60x speedup and saves 73.68% energy compared to an ARM.

关键词： Time complexity Computer architecture parallel processing Computational modeling Resistance Switches Random access memory

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Accurate Answers Selection and Expert Recommendation in Community Question Answers System

Accurate Answers Selection and Expert Recommendation in Comm...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Chetan J. Awati Suresh S. Shirgave Veera S. Raval Shivaji University Kolhapur Kolhapur India DKTE Society's Textile and Engineering Institute Kolhapur India

Question answering framework is further insensitive for the user. these days, user ask the question on the web and accordingly they will get the answer for that question, since perusing is the primary requirement for every individual. By measuring the user request, the system will grant with answer. However, the calculation time has increased by just holding up the time and same sort of inquiries posed by various user, where system requires to offer parallel responses regularly to various users. To stay away from this, `PLANE' technique is proposed to tentatively rank the candidates extracted from the suitable question pool. When user ask few questions, at that point system offer answers for that question in positioning structure and the system recommends most conspicuous evaluation answer to the user. Heceforth, this research work proposes the expert recommendation system to provide suitable answer for the question, which is polled through the user and in addition the proposed research work set up a regular circumstance sentence level clustering strategy in which a single question can get multiple answers. Mainly, the system provides appropriate answer to the question raised by the user.

关键词： Training Annotations Semantics Manuals Knowledge discovery Control systems Natural language processing

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