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检索条件"任意字段=IEEE 34th Conference on Local Computer Networks"

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Comparative Evaluation of Small-Scale Residential PV System Applications in a Developed and a Developing Economy

Comparative Evaluation of Small-Scale Residential PV System ...

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2018 ieee 7th World conference on Photovoltaic Energy Conversion (WCPEC) (A Joint conference of 45th ieee PVSC, 28th PVSEC & 34th EU PVSEC)

作者： Kala Meah Hasan Ali Department of Engineering and Computer Science York College of Pennsylvania York United States Local Government Engineering Department (LGED) Executive Engineer’s Office Cox’s Bazar Bangladesh

this paper studies the impacts and appropriateness of small-scale photovoltaic (PV) system in the USA and Bangladesh. the cost of a residential PV system does not differ between a developed and developing country, but the break-even period is shorter in a developing country than in a developed country. this paper investigates how this expensive energy source is making the greatest impact in developing countries by improving health, education, and environment.

关键词： Solar radiation Photovoltaic systems Economics Finance Carbon dioxide Education

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Lightweight Monolithic Microcell CPV for Space

Lightweight Monolithic Microcell CPV for Space

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2018 ieee 7th World conference on Photovoltaic Energy Conversion (WCPEC) (A Joint conference of 45th ieee PVSC, 28th PVSEC & 34th EU PVSEC)

作者： Christian J. Ruud Jared S. Price Brent Fisher Baomin Wang Noel C. Giebink Department of Materials Science and Engineering The Pennsylvania State University University Park Pennsylvania USA School of Electrical Engineering and Computer Science The Pennsylvania State University University Park Pennsylvania USA H-NU Systems LLC Bethesda Maryland USA

Concentrating photovoltaics (CPV) can increase the efficiency and reduce the cost of photovoltaic power in space. We introduce a new monolithic, ultrathin, and lightweight CPV paradigm based on a transfer-printed microscale solar cell array. In our reflective design, the microcell array is embedded in a radiation-tolerant glass optic that delivers 83% optical efficiency with a ±7° acceptance angle at 32× geometric gain. the system is

关键词： Optical reflection Microcell networks Glass Geometrical optics Optical device fabrication Optical refraction Adaptive optics

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Political Affiliation and Rooftop Solar Adoption in New York and Texas

Political Affiliation and Rooftop Solar Adoption in New York...

引用

2018 ieee 7th World conference on Photovoltaic Energy Conversion (WCPEC) (A Joint conference of 45th ieee PVSC, 28th PVSEC & 34th EU PVSEC)

作者： Deborah A. Sunter John Dees Sergio Castellanos Duncan Callaway Daniel M. Kammen Renewable and Appropriate Energy Laboratory University of California Berkeley CA USA Energy and Resources Group University of California Berkeley CA USA California Institute for Energy and Environment University of California Berkeley CA USA Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Goldman School of Public Policy University of California Berkeley CA USA

Renewable energy is often presented as a partisan issue. However, solar incentives have made rooftop PV attractive to many consumers, regardless of political affiliation. this paper quantifies the role of political affiliation in rooftop PV adoption for the Democratic-leaning state of New York and Republican-leaning state of Texas. High resolution rooftop PV installation data from Google Project Sunroof is combined with election data from the Harvard Election Data Archive. We applied a bootstrapped LOWESS model to fit local linear relationships between rooftop PV adoption and both voting behavior and household income. In doing so, we find that Republican-majority communities are installing rooftop PV at equal or greater rates than Democraticmajority communities. While Republican politicians may be less likely than Democrats to support renewable energy, this partisan division is not found at the voter level.

关键词： Voting Renewable energy sources Portfolios Buildings Google Standards Indexes

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the loss surface of deep and wide neural networks 34

The loss surface of deep and wide neural networks

引用

34th International conference on Machine Learning, ICML 2017

作者： Nguyen, Quynh Hein, Matthias Department of Mathematics and Computer Science Saarland University Germany

ISBN: (纸本)9781510855144

While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal. Copyright © 2017 by the author(s).

关键词： Deep neural networks

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CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural networks 34

CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Proc...

引用

34th ieee International conference on computer Design (ICCD)

作者： Han, Xushen Zhou, Dajiang Wang, Shihao Kimura, Shinji Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781509051427

Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. this paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/offchip data allocation, data flow optimization and data reuse. the proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.

关键词： convolutional neural networks FPGA memory bandwidth reconfigurable processor backward propagation

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DSCNN: Hardware-Oriented Optimization for Stochastic Computing Based Deep Convolutional Neural networks 34

DSCNN: Hardware-Oriented Optimization for Stochastic Computi...

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34th ieee International conference on computer Design (ICCD)

作者： Li, Zhe Ren, Ao Li, Ji Qiu, Qinru Wang, Yanzhi Yuan, Bo Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13224 USA Univ Southern Calif Dept Elect Engn Los Angeles CA 90089 USA CUNY Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781509051427

Deep Convolutional Neural networks (DCNN), a branch of Deep Neural networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters;it is restricted to extend the applications onto mobile/wearable devices and Internet-of-things (IoT) entities due to high power/energy consumption. Stochastic Computing is a promising method to overcome this shortcoming used in specific hardware-based systems. Many complex arithmetic operations can be implemented with very simple hardware logic in the SC framework, which alleviates the extensive computation complexity. the exploration of network-wise optimization and the revision of network structure with respect to stochastic computing based hardware design have not been discussed in previous work. In this paper, we investigate Deep Stochastic Convolutional Neural Network (DSCNN) for DCNN using stochastic computing. the essential calculation components using SC are designed and evaluated. We propose a joint optimization method to collaborate components guaranteeing a high calculation accuracy in each stage of the network. the structure of original DSCNN is revised to accommodate SC hardware design's simplicity. Experimental Results show that as opposed to software inspired feature extraction block in DSCNN, an optimized hardware oriented feature extraction block achieves as higher as 59.27% calculation precision. And the optimized DSCNN can achieve only 3.48% network test error rate compared to 27.83% for baseline DSCNN using software inspired feature extraction block.

关键词： Deep Learning Deep Convolutional Neural networks Stochastic Computing Hardware-oriented Cooptimization

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A Model for Application Slowdown Estimation in On-Chip networks and Its Use for Improving System Fairness and Performance 34

A Model for Application Slowdown Estimation in On-Chip Netwo...

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34th ieee International conference on computer Design (ICCD)

作者： Xiang, Xiyue Ghose, Saugata Mutlu, Onur Tzeng, Nian-Feng Univ Louisiana Lafayette Lafayette LA 70504 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781509051427

In a network-on-chip (NoC) based system, the NoC is a shared resource among multiple processor cores. Network requests generated by different applications running on different cores can interfere with each other, leading to a slowdown in performance of each application. the degree of slowdown introduced by this interference varies for each application, as it depends on (1) the sensitivity of the application to NoC performance, and (2) network traffic induced by other applications running concurrently on the system. In modern systems, NoC interference is largely uncontrolled, and therefore some applications unfairly slow down much more than others. this can lead to overall system performance degradation, prevent fair progress of different applications, and cause starvation of unfairly-treated applications. Our goal is to accurately model the slowdown of each application executing on the system due to NoC interference at runtime, and to use this information to improve system performance and reduce unfairness. To this end, we propose the NoC Application Slowdown (NAS) Model, the first online model that accurately estimates how much network delays due to interference contribute to the overall stall time of each application. the key idea of NAS is to determine how the delays induced at each level of network data transmission overlap with each other, and to use the overlap information to calculate the net impact of the delays on application stall time. Our model determines the application slowdowns at runtime with a very low error rate, averaging 4.2% over 90 multiprogrammed workloads for an 8x8 mesh network. We use NAS to develop Fairness-Aware Source throttling (FAST), a mechanism that employs slowdown predictions to control the network injection rates of applications in a way that minimizes system unfairness. Our results over a variety of multiprogrammed workloads show that FAST improves average system fairness and performance by 9.5% and 5.2%, respectively.

关键词： Network-on-chip

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Stochastic neuromorphic learning machines for weakly labeled data

Stochastic neuromorphic learning machines for weakly labeled...

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ieee International conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Emre Neftci Department of Cognitive Sciences University of California Irvine

At learning tasks where humans typically outperform computers, neuromorphic learning machines can have potential advantages in learning in terms of power and complexity compared to mainstream technologies. Here, we present Synaptic Sampling Machines (S2M), a class of stochastic neural networks that use stochasticity at the connections (synapses) to implement energy efficient semi- and unsupervised learning for weakly or unlabeled data. Stochastic synapses play the dual role of a regularizer during learning and a mechanism for implementing stochasticity in neural networks. We show a S2M network architecture that is well suited for a dedicated digital implementation, that is potentially hundredfold more energy efficient compared to equivalent algorithms operating on GPUs.

关键词： Neuromorphics Neurons Biological neural networks Stochastic processes Training Hardware Semisupervised learning

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Design techniques of eNVM-enabled neuromorphic computing systems

Design techniques of eNVM-enabled neuromorphic computing sys...

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ieee International conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Chang Song Beiye Liu Chenchen Liu Hai Li Yiran Chen Electrical and Computer Engineering Department University of Pittsburgh Pittsburgh PA USA

the recently emerged research on “neuromorphic computing”, which stands for hardware acceleration of brain-inspired computing, has become one of the most active research areas in computer engineering. In this invited paper, we start with a background introduction of neuromorphic computing, followed by some examples of hardware acceleration schemes of learning and neural network algorithms on emerging nonvolatile memory (eNVM)-based neuromorphic computing engine. At the end, we share our prospects on the future technology challenges and advances of neuromorphic computing.

关键词： Memristors Training Neuromorphics Neurons Hardware Biological neural networks Resistance

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Quantifying the difference in resource demand among classic and modern NoC workloads

Quantifying the difference in resource demand among classic ...

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ieee International conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Amirhossein Mirhosseini Mohammad Sadrosadati Maryam Zare Hamid Sarbazi-Azad Department of Computer Science University of Illinois at Urbana-Champaign USA Department of Computer Engineering Sharif University of Technology Tehran Iran Computer Science School Institute for Researches in Fundamental Sciences Tehran Iran

this paper quantifies the difference in resource demand between modern and classic NoC workloads. In the paper, we show that modern workloads are able to better utilize higher numbers of VCs and smaller C factors in order to attain performance and energy efficiency. this is because of the high throughput and possible local congestions in their traffic pattern. As a result, such workloads are more suitable for concurrency and redundancy energy reduction techniques where the voltage and frequency are reduced simultaneously and the increased power budget is used for introducing additional resources to the network in order to improve the performance.

关键词： throughput computer architecture Network topology System-on-chip Bandwidth Topology Power demand

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