8 research outputs found

    Sigmoid Function Implementation Using the Unequal Segmentation of Differential Lookup Table and Second Order Nonlinear Function

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    This paper discusses the artificial neural network (ANN) implementation into a field programmable gate array (FPGA). One of the most difficult problem encounters is the complex equation of the activation function namely sigmoid function. The sigmoid function is used as learning function to train the neural network while its derivative is used as a network activation function for specifying the point at which the network should switch to a true state. In order to overcome this problem, two-steps approach which combined the unequal segmentation of the differential look-up table (USdLUT) and the second order nonlinear function (SONF) is proposed. Based on the analysis done, the deviation achieved using the proposed method is 95%. The result obtained is much better than the previous implementation that uses equal segmentation of differential look-up table

    WeAbDeepCNN: Weighted Average Model and ASSCA based Two Level Fusion Scheme For Multi-Focus Images

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    905-914Fusion of images is a strategy that merges various moderately focused images or non-focused images of a single scene to generate a fully focused, clear and sharp image. The goal of this research is to discover the focused regions and further combination of focused regions of different source images into solitary image. However, there exist several issues in image fusion that involves contrast reduction, block artifacts, and artificial edges. To solve this issue, a two level fusion scheme has been devised, which involves weighted average model along with Atom Search Sine Cosine algorithm-based Deep Convolutional Neural Network (ASSCA-based Deep CNN) and may be abbreviated as “WeAbDeepCNN” i.e. weighted average model and ASSCA based Deep CNN. In the study two images are fed to initial fusion module, which is performed using weighted average model. The fusion score are generated whose values are determined in an optimal manner. Thus, final fusion is performed using proposed ASSCA-based Deep CNN. The Deep CNN training is carried out with proposed ASSCA, which is devised by combining Sine Cosine Algorithm, abbreviated as SCA, as well as atom search optimization (ASO). The proposed ASSCA-based Deep CNN offers improved performance in contrast to current state of the art techniques with a highest value 1.52 of mutual information (MI), with a highest value of 32.55 dB of maximum Peak Signal to Noise Ratio i.e. PSNR as well as value of 7.59 of Minimum Root Mean Square Error (RMSE)

    WeAbDeepCNN: Weighted Average Model and ASSCA based Two Level Fusion Scheme For Multi-Focus Images

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    Fusion of images is a strategy that merges various moderately focused images or non-focused images of a single scene to generate a fully focused, clear and sharp image. The goal of this research is to discover the focused regions and further combination of focused regions of different source images into solitary image. However, there exist several issues in image fusion that involves contrast reduction, block artifacts, and artificial edges. To solve this issue, a two level fusion scheme has been devised, which involves weighted average model along with Atom Search Sine Cosine algorithm-based Deep Convolutional Neural Network (ASSCA-based Deep CNN) and may be abbreviated as “WeAbDeepCNN” i.e. weighted average model and ASSCA based Deep CNN. In the study two images are fed to initial fusion module, which is performed using weighted average model. The fusion score are generated whose values are determined in an optimal manner. Thus, final fusion is performed using proposed ASSCA-based Deep CNN. The Deep CNN training is carried out with proposed ASSCA, which is devised by combining Sine Cosine Algorithm, abbreviated as SCA, as well as atom search optimization (ASO). The proposed ASSCA-based Deep CNN offers improved performance in contrast to current state of the art techniques with a highest value 1.52 of mutual information (MI), with a highest value of 32.55 dB of maximum Peak Signal to Noise Ratio i.e. PSNR as well as  value of 7.59 of Minimum Root Mean Square Error (RMSE)

    Towards Power- and Energy-Efficient Datacenters

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    As the Internet evolves, cloud computing is now a dominant form of computation in modern lives. Warehouse-scale computers (WSCs), or datacenters, comprising the foundation of this cloud-centric web have been able to deliver satisfactory performance to both the Internet companies and the customers. With the increased focus and popularity of the cloud, however, datacenter loads rise and grow rapidly, and Internet companies are in need of boosted computing capacity to serve such demand. Unfortunately, power and energy are often the major limiting factors prohibiting datacenter growth: it is often the case that no more servers can be added to datacenters without surpassing the capacity of the existing power infrastructure. This dissertation aims to investigate the issues of power and energy usage in a modern datacenter environment. We identify the source of power and energy inefficiency at three levels in a modern datacenter environment and provides insights and solutions to address each of these problems, aiming to prepare datacenters for critical future growth. We start at the datacenter-level and find that the peak provisioning and improper service placement in multi-level power delivery infrastructures fragment the power budget inside production datacenters, degrading the compute capacity the existing infrastructure can support. We find that the heterogeneity among datacenter workloads is key to address this issue and design systematic methods to reduce the fragmentation and improve the utilization of the power budget. This dissertation then narrow the focus to examine the energy usage of individual servers running cloud workloads. Especially, we examine the power management mechanisms employed in these servers and find that the coarse time granularity of these mechanisms is one critical factor that leads to excessive energy consumption. We propose an intelligent and low overhead solution on top of the emerging finer granularity voltage/frequency boosting circuit to effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost, improving energy efficiency without sacrificing the quality of services. The final focus of this dissertation takes a further step to investigate how using a fundamentally more efficient computing substrate, field programmable gate arrays (FPGAs), benefit datacenter power and energy efficiency. Different from other types of hardware accelerations, FPGAs can be reconfigured on-the-fly to provide fine-grain control over hardware resource allocation and presents a unique set of challenges for optimal workload scheduling and resource allocation. We aim to design a set coordinated algorithms to manage these two key factors simultaneously and fully explore the benefit of deploying FPGAs in the highly varying cloud environment.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144043/1/hsuch_1.pd

    Uma arquitetura reconfigurável de Rede Neural Artificial utilizando FPGA.

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    Este trabalho apresenta uma nova implementação em hardware de Rede Neural Artificial que permite reconfiguração da arquitetura que é implementada. Este tipo de design é importante em aplicações em que o ambiente varia de tal maneira que é necessária uma mudança na arquitetura da Rede Neural para que os resultados continuem adequados. A topologia usada foi a MultiLayer Perceptron, onde os neurônios são organizados em camadas e cada camada recebe como entrada as saídas da camada anterior, ou seja, elas têm uma execução sequencial. A implementação desenvolvida permite mudanças no número de neurônios de cada camada, número de entradas e saídas da Rede Neural e do tipo de função de ativação que os neurônios de cada camada irão executar. Apesar de implementada em FPGA, a Rede Neural proposta não depende de nenhum de seus modelos, já que nenhum bloco proprietário foi usado. Esta característica permite que o sistema aqui proposto seja implementado com facilidade em um circuito integrado a ser usado em implantes médicos, por exemplo. A Rede Neural foi submetida a três testes práticos que provaram seu funcionamento e os resultados em termos de erros atingidos foram analisados

    South Dakota State University Undergraduate General Catalog 2008-2009

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    The Parallel FDFM Processor Core Approach for Neural Networks

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