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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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A Deep Learning Approach for Volterra Kernel Extraction for Time Domain Simulation of Weakly Nonlinear Circuits 69

A Deep Learning Approach for Volterra Kernel Extraction for ...

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69th IEEE Electronic Components and Technology conference (ECTC)

作者： Thong Nguyen Wang, Xinying Chen, Xu Schutt-Aine, Jose Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

ISBN: (纸本)9781728114989

Volterra kernels are well known to be the multidimensional extension of the impulse response of a linear time invariant (LTI) system. It can be used to accurately model weakly nonlinear, specifically, polynomial nonlinearity systems. It has been used in the past for white-box model order reduction (MOR) to model frequency-domain performance metric quantities such as distortion in power amplifiers (PA). In this paper, we train a neural network from time-domain response of high-speed link buffers to extract multiple high-order kernels at once. Once the kernels are extracted, they can fully characterize the dynamics of the buffers of interest. Using the kernels, we demonstrate that time-domain response is straight-forward to obtain using super-, or multi-dimensional convolution. Previous work has used a shallow feed-forward neural network to train the system by using Gaussian noise as the identification signal. This is not convenient for the method to be compatible with existing computer-aided design tools. In this work, we directly use a pseudo random bit sequence (PRBS) to train the network. The proposed technique is more challenging because the PRBS has flat regions which have highly rich frequency spectrum and requires longer memory length, but allows the method to be compatible with existing simulation programs. We investigate different topologies including feed-forward neural network and recurrent neural network. Comparisons between training phase, inference phase, convergence are presented using different neural network topologies. The paper presents a numerical example using a 28Gbps data rate PAM4 transceiver to validate the proposed method against traditional simulation methods such as IBIS or SPICE level simulation for comparison in speed and accuracy. Using Volterra kernels promises a novel way to perform accurate nonlinear circuit simulation in the LTI system framework which is already well known and well developed. It can be conveniently incorporated into

关键词： Volterra Series Volterra Kernels Weakly Nonlinear Time-invariant Neural Network Behavioral Modeling Signal Integrity PAM4 high-speed channel

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Speeding Up SpMV for Power-Law Graph Analytics by Enhancing Locality & Vectorization 20

Speeding Up SpMV for Power-Law Graph Analytics by Enhancing ...

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Supercomputing conference

作者： Serif Yesil Azin Heidarshenas Adam Morrison Josep Torrellas University of Illinois at Urbana-Champaign Blavatnik School of Computer Science Tel Aviv University

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Graph analytics applications often target large-scale web and social networks, which are typically power-law graphs. Graph algorithms can often be recast as generalized Sparse Matrix-Vector multiplication (SpMV) operations, making SpMV optimization important for graph analytics. However, executing SpMV on large-scale power-law graphs results in highly irregular memory access patterns with poor cache utilization. Worse, we find that existing SpMV locality and vectorization optimizations are largely ineffective on modern out-of-order (OOO) processors-they are not faster (or only marginally so) than the standard Compressed Sparse Row (CSR) SpMV implementation. To improve performance for power-law graphs on modern OOO processors, we propose Locality-Aware Vectorization (LAV). LAV is a new approach that leverages a graph's power-law nature to extract locality and enable effective vectorization for SpMV-like memory access patterns. LAV splits the input matrix into a dense and a sparse portion. The dense portion is stored in a new representation, which is vectorization-friendly and exploits data locality. The sparse portion is processed using the standard CSR algorithm. We evaluate LAV with several graphs on an Intel Skylake-SP processor, and find that it is faster than CSR (and prior approaches) by an average of 1.5x. LAV reduces the number of DRAM accesses by 35% on average, with only a 3.3% memory overhead.

关键词： Out of order program processors Social networking (online) Random access memory Sparse matrices Optimization Standards

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AN ENTERPRISE CYBERSECURITY STRATEGY FOR FEDERAL CRITICAL INFRASTRUCTURE modernization 18

AN ENTERPRISE CYBERSECURITY STRATEGY FOR FEDERAL CRITICAL IN...

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18th Integrated Communications, Navigation and Surveillance Systems (ICNS) conference

作者： Bradley, Ian D. Norville, Brian Georgetown Univ Washington DC 20057 USA

ISBN: (纸本)9781538656792

Federal systems are constantly faced with risks due to the ever-evolving cyber threat landscape. Cyber threats can cause anything from degradation of system functionality to data theft. Depending on the criticality of the Federal system, a cyber-event could be catastrophic, causing a significant financial loss to the Federal government, potentially impacting the privacy or safety of the American public, thus being categorized as Critical Infrastructure. In the past year, modernization of Federal systems has gained much attention. In 2017, The Executive Office of President finalized a report on the Federal IT modernization [1], as well as the Presidential Executive Order on Strengthening the Cybersecurity of Federal Networks and Critical Infrastructure [2]. In many cases, Federal Critical Infrastructure may often be comprised of legacy systems, which consist of aging technologies, program languages or hardware. The cost of the sustainment and operation of legacy assets will continue to increase over time and become more difficult to protect, as outdated technologies become obsolete or unsupported. The existence of legacy technology may also limit the ability of the adoption of new capabilities. To address these issues, Federal agencies must incrementally reduce the usage of legacy assets through modernization. The Modernizing Government Technology Act of 2017 or MGT Act was passed by the House of Representatives on May 17, 2017, and was recently passed into law on December 12, 2017. The MGT Act of 2017 is a bipartisan effort, which calls for agencies to modernize their aging systems. The MGT Act builds upon the Federal IT Acquisition Reform Act (FITARA), creating the FITARA scorecard, a grading system used to monitor the modernization efforts of Federal agencies and empower CIOs to take action. The score is calculated based on CIO performance, risk management, data center optimization and other factors. In the latest FITARA Scorecard released in November of 2017, bot

关键词： Critical infrastructure computer security Aging NIST

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imBBO: An Improved Biogeography-Based Optimization Algorithm 13th

imBBO: An Improved Biogeography-Based Optimization Algorithm

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13th International conference on Green, Pervasive, and Cloud Computing, GPC 2018

作者： Shi, Kai Yu, Huiqun Fan, Guisheng Yang, Xingguang Song, Zheng Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China The Third Research Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9783030150921

Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed. © 2019, Springer Nature Switzerland AG.

关键词： Global optimization

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high-responsivity photodiodes heterogeneously integrated on silicon nitride waveguides

High-responsivity photodiodes heterogeneously integrated on ...

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Integrated Photonics Research, Silicon and Nanophotonics, IPRSN 2019

作者： Yu, Qianhuan Ye, Nan Gao, Junyi Sun, Keye Xie, Linli Srinivasan, Kartik Zervas, Michael Navickaite, Gabriele Geiselmann, Michael Beling, Andreas Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Microsystems and Nanotechnology Division National Institute of Standards and Technology GaithersburgMD United States LIGENTEC SA EPFL Innovation Park Bâtiment C LausanneCH-1015 Switzerland

We demonstrate InGaAs/InP photodiodes on Si3N4 waveguides with record-high external (internal) responsivities of 0.68 A/W (0.8 A/W) and 0.24 A/W (0.6 A/W) at 1550 nm and 1064 nm, respectively. Balanced photodiodes hav... 详细信息

ISBN: (纸本)9781943580644

关键词： Photodiodes

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A novel approach to detect outer retinal tubulation using U-Net in SD-OCT images 15

A novel approach to detect outer retinal tubulation using U-...

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15th International conference on Signal Image Technology and Internet Based Systems, SISITS 2019

作者： Megyeri, Istvan Katona, Melinda Nyul, Laszlo G. Department of Computer Algorithms and Artificial Intelligence Hungary Department of Image Processing and Computer Graphics University of Szeged Hungary

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156866

Optical Coherence Tomography (OCT) has become a basic non-invasive tool in diagnosing and following different types of eye diseases. This technique can produce high-resolution cross-sectional images of retinal layers. Outer retinal tubulation (ORT) is one of the detectable biomarker by SD-OCT. ORTs defined as hyporeflective, tubular structures with hyperreflective borders or reversed within the retina and appear in many retinal diseases, including age-related macular degeneration (AMD). Our aim is to develop an automatic method that can efficiently characterize ORT biomarker. Detection of this biomarker can be challenging because of its variable size, location, and reflectivity. In this paper, we present a fully convolutional U-Net based architecture to detect ORT. The proposed approach is evaluated using a dataset annotated by ophthalmologists. One of the main challenges was the limited amount of training data that we resolve with real-time augmentation during training and using nested cross-validation. Our method achieved near human performance reaching an overall object-based recall score of 0.847 and Dice score of 0.579 on the test set. © 2019 IEEE.

关键词： Optical tomography

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Beyond Peak performance: Comparing the Real performance of AI-Optimized FPGAs and GPUs

Beyond Peak Performance: Comparing the Real Performance of A...

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IEEE International conference on Field-programmable Technology (FPT)

作者： Andrew Boutros Eriko Nurvitadhi Rui Ma Sergey Gribok Zhipeng Zhao James C. Hoe Vaughn Betz Martin Langhammer University of Toronto and Vector Institute Programmable Solutions Group Intel Corporation Carnegie Mellon University

ISBN: (纸本)9781665423021;9781665446228

The growing importance and compute demands of artificial intelligence (AI) have led to the emergence of domain-optimized hardware platforms. For example, Nvidia GPUs introduced specialized tensor cores for matrix operations to speed up deep learning (DL) computation, resulting in very high peak throughput up to 130 int8 TOPS in the T4 GPU. Recently, Intel introduced its first AI-optimized 14nm FPGA, the Stratix 10 NX, with in-fabric AI tensor blocks that offer estimated peak performance up to 143 int8 TOPS, comparable to 12nm GPUs. However, what matters in practice is not the peak performance but the actual achievable performance on target workloads. This depends mainly on the utilization of the tensor units, and the system-level overheads to send data to/from the accelerator. This paper presents the first performance evaluation of Intel's AI-optimized FPGA, the Stratix 10 NX, in comparison to the latest accessible AI-optimized GPUs, the Nvidia T4 and V100, on a large suite of real-time DL inference workloads. We enhance a re-implementation of the Brainwave NPU overlay architecture to utilize the FPGA's AI tensor blocks, and develop toolchain support that allows users to program tensor blocks purely through software, without FPGA EDA tools in the loop. We first compare the Stratix 10 NX NPU against Stratix 10 GX/MX versions with no tensor blocks, and then present detailed core compute and system-level performance comparisons to the T4 and V100 GPUs. We show that our enhanced NPU on Stratix 10 NX achieves better tensor block utilization than GPUs, resulting in 24× and 12× average compute speedups over the T4 and V100 GPUs at batch-6. Even with relaxed latency constraints that allow a batch size of 32, we still achieve average speedups of 5× and 2× against T4 and V100 GPUs, respectively. On a system-level, the FPGA's fine-grained flexibility with its integrated 100 Gbps Ethernet allows for remote access at 10× and 2× less system overhead latency than local access to a

关键词： Tensors Graphics processing units Ethernet computer architecture Tools Throughput Software

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Analysis of high Mg-incorporation into GaN via PAMBE modulation doping and molecular dynamics simulations

Analysis of high Mg-incorporation into GaN via PAMBE modulat...

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2019 International conference on Compound Semiconductor Manufacturing Technology, CS MANTECH 2019

作者： Ismail, Fawad Hassan Landi, Matthew Kelly, Frank Putnam Kim, Kyekyoon Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This work concerns the investigation of high Mg incorporation in GaN. Mg is a widely used p-dopant for GaN, which could benefit from high dopant concentration, as well as high activation, through novel growth methods. This has the potential to create high performance GaN devices, owing to the use of quality p-doped GaN. We carry out this investigation on two fronts. First, we explore the feasibility of PAMBE based modulation doping as the growth method of choice for high incorporation. We conduct growth experiments based on variations of Ga, N, and Mg fluxes, and assess the quality of the films and resulting p-doped samples using SIMS, XRD, CV, and Hall measurements. Second, we carry out atomistic simulations, using MD and KMC, to develop insights into the growth of GaN crystals, guiding the growth process as a result. We incorporate 3-body Tersoff potentials, developed specifically for GaN, to simulate GaN growth through MD. We extract migration, absorption, and desorption statistics, and utilize these transition rates in a computationally efficient method, KMC. Use of high quality p-doped GaN in power devices will help further advance this technology in terms of efficiency, compactness, and power handling. © 2019 CS Mantech. All rights reserved.

关键词： Magnesium

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EXPERIMENTAL STUDY OF FLEXIBLE ELECTROHYDRODYNAMIC CONDUCTION PUMPING FOR ELECTRONICS COOLING

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ASME International Technical conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems

作者： Tobar, Nicolas Vayas Christidis, Pavolas N. O'Connor, Nathaniel J. Talmor, Michal Seyed-Yagoobi, Jamal Worcester Polytech Inst Dept Mech Engn Multiscale Heat Transfer Lab Worcester MA 01609 USA

ISBN: (纸本)9780791851920

As modern day electronics develop, electronic devices become smaller, more powerful, and are expected to operate in more diverse configurations. However, the thermal control systems that help these devices maintain stable operation must advance as well to meet the demands. One such demand is the advent of flexible electronics for wearable technology, medical applications, and biology-inspired mechanisms. This paper presents the design and performance characteristics of a proof of concept for a flexible Electrohydrodynamic (EHD) pump, based on EHD conduction pumping technology in macro- and meso-scales. Unlike mechanical pumps, EHD conduction pumps have no moving parts, can be easily adjusted to the micro-scale, and have been shown to generate and control the flow of refrigerants for electronics cooling applications. However, these pumping devices have only been previously tested in rigid configurations unsuitable for use with flexible electronics. In this work, for the first time, the net flow generated by flexible EHD conduction pumps is measured on a flat-plane and in various bending configurations. In this behavioral characteristics study, the results show that the flexible EHD conduction pumps are capable of generating significant flow velocities in all size scales considered in this study, with and without bending. This study also proves the viability of screen printing as a manufacturing method for these pumps. EHD conduction pumping technology shows potential for use in a wide range of terrestrial and space applications, including thermal control of rigid as well as flexible electronics, flow generation and control in micro-scale heat exchangers and other thermal devices, as well as cooling of high power electrical systems, soft robotic actuators, and medical devices.

关键词： Heat conduction Electrohydrodynamics computer cooling

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Improved Algorithms for Zero Shot Image Super-Resolution with Parametric Rectifiers 15th

Improved Algorithms for Zero Shot Image Super-Resolution wit...

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15th International conference on Advanced Data Mining and Applications (ADMA)

作者： Zhu, Jiayi An, Senjian Liu, Wanquan Li, Ling Curtin Univ Sch Elect Engn Comp & Math Sci Kent St Bentley WA 6102 Australia

ISBN: (纸本)9783030352318;9783030352301

Recently, a novel Zero-Shot Super-Resolution (ZSSR) method is proposed to generate high-resolution (HR) images from their low-resolution (LR) counterparts. ZSSR employs a convolutional neural network (CNN) to represent transformations from LR images to HR images and is trained on a single image. ZSSR achieves state-of-the-art performance on both real low-resolution images (i.e., historic images, and images taken with a mobile phone) and several benchmark datasets (e.g., Set 5 and Set 14 to name a few). However, the training of the CNN network of ZSSR is not stable since rectifier is used as the activation function and a custom learning rate adjustment policy is proposed in ZSSR. In this paper, we use parametric rectifier as the activation function and present an improved algorithm for the training of ZSSR. Experimental results demonstrate that the proposed method outperforms ZSSR in terms of both reconstruction accuracy and speed on two benchmark datasets: Set 5 and Set 14, respectively.

关键词： Single Image Super-Resolution Unsupervised computer vision

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