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Improvement in Deep networks for Optimization Using eXplainable Artificial Intelligence 10

Improvement in Deep Networks for Optimization Using eXplaina...

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10th International conference on Information and Communication Technology Convergence (ICTC) - ICT Convergence Leading the Autonomous Future

作者： Lee, Jin Ha Shin, Ik Hee Jeong, Sang Gu Lee, Seung-Ik Zaheer, Muhamamad Zaigham Univ Sci & Technol Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781728108933

With the recent advancements in the field of explainable artificial intelligence (XAI), which covers the domain of turning deep network architectures from black boxes to comprehensible structures, it became easy to understand what goes on inside a network when it predicts an output. Many researchers have successfully shown the 'thought process' behind a network's decision making. This rich and interesting information has not been utilized beyond the scope of visualizations once the training finish. In this work, a novel idea to utilize this insight to the network as a training parameter is proposed. Layer-wise Relevance Propagation (LRP), which obtains the effect of each neuron towards the output of the whole network, is used as a parameter, along with learning rate and network weights, to optimize the training. Various intuitive formulations have been proposed, and the results of the experiments on MNIST and CIFAR-10 datasets have been reported in this paper. Our proposed methodologies show better or comparable performances against conventional optimization algorithms. This would open a new dimension of research to explore the possibility of using XAI in optimizing the training of neural networks.

关键词： explainable artificial intelligence neural network optimization layer-wise relevance propagation

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Study of Prevention of Mode Collapse in Generative Adversarial network (GAN)

Study of Prevention of Mode Collapse in Generative Adversari...

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conference on Information and Communication Technology (CICT)

作者： Bhagyashree Vandana Kushwaha G. C. Nandi Center of Intelligent Robotics Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (数字)9780738124476

ISBN: (纸本)9781665419291

With the advancement of Deep Neural network and its increased applications, requirement of data has increased exponentially. To fulfil this requirement Deep generative models specifically Generative Adversarial networks (GANs) have emerged as a very powerful tool. However, tuning GAN parameters are extremely difficult due to its instability and it is very prone to miss modes while training, which is termed as Mode Collapse. Mode collapse leads generators to generate the images of a particular mode while ignoring the other mode classes. In the present research, we propose a novel method to deal with Mode Collapse by using multiple generator architecture. Initially, We have shown the comparison of different GAN architectures which deals with the Mode collapse problem. We use Inception score (IS) as a evaluation metric to evaluate the performance of GAN. We started analysing GAN on a simple dataset (MNIST) using DCGAN architecture. To produce better results, the present work describes the implementation of two other different approaches. We have experimented on Wasserstein GAN (WGAN) which improves GANs training by adopting a different metric which is Wasserstein distance for calculating the distance between two probability distributions. Subsequently we have proposed the approach of multiple generator GAN architecture which uses multiple generators to provide a better solution to the missing modes problem. We evaluate our approach on several datasets (MNIST, CIFAR-10, SVHN, CelebA Face dataset) with encouraging results compared to the other existing architectures.

关键词： Generators Generative adversarial networks Gallium nitride Training Measurement Linear programming Standards

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ALWANN: Automatic Layer-Wise Approximation of Deep Neural network Accelerators without Retraining 38

ALWANN: Automatic Layer-Wise Approximation of Deep Neural Ne...

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38th ieee/ACM International conference on Computer-Aided Design (ICCAD)

作者： Mrazek, Vojtech Vasicek, Zdenek Sekanina, Lukas Hanif, Muhammad Abdullah Shafique, Muhammad Brno Univ Technol IT4Innovat Ctr Excellence Fac Informat Technol Brno Czech Republic Tech Univ Wien TU Wien Vienna Austria

ISBN: (纸本)9781728123509

The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to recover from the accuracy loss caused by the use of approximate operations. However, retraining of complex DNNs does not scale well. In this paper, we demonstrate that efficient approximations can be introduced into the computational path of DNN accelerators while retraining can completely be avoided. ALWANN provides highly optimized implementations of DNNs for custom low-power accelerators in which the number of computing units is lower than the number of DNN layers. First, a fully trained DNN (e.g., in TensorFlow) is converted to operate with 8-bit weights and 8-bit multipliers in convolutional layers. A suitable approximate multiplier is then selected for each computing element from a library of approximate multipliers in such a way that (i) one approximate multiplier serves several layers, and (ii) the overall classification error and energy consumption are minimized. The optimizations including the multiplier selection problem are solved by means of a multi-objective optimization NSGA-II algorithm. In order to completely avoid the computationally expensive retraining of DNN, which is usually employed to improve the classification accuracy, we propose a simple weight updating scheme that compensates the inaccuracy introduced by employing approximate multipliers. The proposed approach is evaluated for two architectures of DNN accelerators with approximate multipliers from the open-source "EvoApprox" library, while executing three versions of ResNet on CIFAR-10. We report that the proposed approach saves 30% of energy needed for multiplication in convolutional layers of ResNet-50 while the accuracy is degraded by only 0.6% (0.9% for the ResNet-14).

关键词： approximate computing deep neural networks computational path ResNet CIFAR-10

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Performance optimization on LoRa networks through assigning radio parameters

Performance optimization on LoRa networks through assigning ...

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ieee International conference on Industrial Technology (ICIT)

作者： Eduardo Sallum Nuno Pereira Mário Alves Max Santos Ponta Grossa Brazil Porto Portugal

ISBN: (数字)9781728157542

ISBN: (纸本)9781728157559

Low Power Wide Area networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5% and 2% of DER, and a number of collisions 11 and 2.5 times smaller than equal-distribution, and random distribution, respectively.

关键词： Optimization Logic gates Payloads Cascading style sheets Physical layer Standards Mixed integer linear programming

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Software-Hardware Co-Design of Multi-Standard Digital Baseband Processor for IoT 22

Software-Hardware Co-Design of Multi-Standard Digital Baseba...

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22nd Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Amor, Hela Belhadj Bernier, Carolynn Univ Grenoble Alpes LETI CEA IC Design Architectures & Embedded Software Dept F-38000 Grenoble France

ISBN: (纸本)9783981926323

This work demonstrates an ultra-low power, software-defined wireless transceiver designed for IoT applications using an open-source 32-bit RISC-V core. The key driver behind this success is an optimized hardware/software partitioning of the receiver's digital signal processing operators. We benchmarked our architecture on an algorithm for the detection of FSK-modulated frames using a RISC-V compatible core and ARM Cortex-M series processors. We use only standard compilation tools and no assembly-level optimizations. Our results show that Bluetooth LE frames can be detected with an estimated peak core power consumption of 1.6 mW on a 28 nm FDSOI technology, and falling to less than 0.6 mW (on average) during symbol demodulation. This is achieved at nominal voltage. Compared to state of the art, our work offers a power efficient alternative to the design of dedicated baseband processors for ultra-low power software-defined radios with a low software complexity.

关键词： IoT LPWA software-defined radio RISC-V

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AOWS: Adaptive and Optimal network Width Search With Latency Constraints

AOWS: Adaptive and Optimal Network Width Search With Latency...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Maxim Berman Leonid Pishchulin Ning Xu Matthew B. Blaschko Gérard Medioni Department of Electrical Engineering Center for Processing Speech and Images KU Leuven Amazon Go

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the target platform. We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware. We first show that we can use a black-box approach to estimate a realistic latency model for a specific inference platform, without the need for low-level access to the inference computation. Then, we design a pairwise MRF to score any channel configuration and use dynamic programming to efficiently decode the best performing configuration, yielding an optimal solution for the network width search. Finally, we propose an adaptive channel configuration sampling scheme to gradually specialize the training phase to the target computational constraints. Experiments on ImageNet classification show that our approach can find networks fitting the resource constraints on different target platforms while improving accuracy over the state-of-the-art efficient networks.

关键词： Training Computational modeling Computer architecture Task analysis Neural networks Hardware Measurement

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Problem of Choosing a DBMS in Modern Information System

Problem of Choosing a DBMS in Modern Information System

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2019 ieee International conference on Advanced Trends in Information Theory, ATIT 2019

作者： openko, Pavel V. Hohoniants, Spartak Yu. Starkova, Olena V. Herasymenko, Kostiantyn V. Yastrebov, Mykola I. Prudchenko, Andrii O. National Defence University of Ukraine Named after Ivan Cherniakhovskyi Kyiv Ukraine Taras Shevchenko National University of Kyiv Department of Network and Internet Technologies Kyiv Ukraine National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Faculty of Radio Engineering Kyiv Ukraine Taras Shevchenko National University of Kyiv Department of Software Engineering Kyiv Ukraine

ISBN: (纸本)9781728161440

this work is devoted to choosing a DBMS in modern information systems. DBMS (Database Management System) is a software package designed to define, manipulate, retrieve and manage data in a database;OLTP (Online Transactional Processing) is a category of data processing that is focused on transaction-oriented tasks and typically involves inserting, updating, and/or deleting small amounts of data in a database;OLAP (Online Analytical Processing) is a computing method that enables users to easily and selectively extract and query data in order to analyze it from different points of view;ACID (an acronym for Atomicity, Consistency, Isolation, Durability) is a concept that Database professionals generally use when evaluating databases and application architectures;SQL (Structured Query Language) is a standardized programming language that's used to manage relational databases and perform various operations on the data in them. © 2019 ieee.

关键词： Query languages

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Performance evaluation of two service recovery strategies in cloud-native radio access networks 21

Performance evaluation of two service recovery strategies in...

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21st International conference on Transparent Optical networks, ICTON 2019

作者： Ramanathan, Shunmugapriya Kondepu, Koteswararao Mirkhanzadeh, Behzad Razo, Miguel Tacca, Marco Valcarenghi, Luca Fumagalli, Andrea Open Network Advanced Research Lab University of Texas Dallas United States Scuola Superiore sant'Anna Pisa Italy

ISBN: (纸本)9781728127798

Through the virtualization of network functions (VNFs) Cloud-native radio access network (C-RAN) architectures bring several advantages to mobile virtual network operators, such as reduced cost, improved flexibility, scalability, and security. However, when these VNFs are affected by Cloud failure or maintenance, the service downtime perceived by the C-RAN mobile user should be minimized whenever *** study investigates experimentally the C-RAN mobile user service downtime in the presence of a failing mobility management entity (MME) when two alternative recovery mechanisms are applied: i) a live migration of the virtual MME from the affected data center to a backup data center, and ii) the use of a pool of geo-diverse MMEs, which is a functionality available in S1-flex supported by openAirInterface. The study leverages a Software Defined networking (SDN) optical backhaul network that consists of open ROADM equipment configurable through the TransportPCE Controller and PROnet resource Orchestrator. The former mechanism should be used as a proactive recovery and therefore activated when the MME failure is predictable, whereas the second mechanism should be used as a reactive recovery and therefore activated to overcome sudden and unexpected MME failure occurrences. © 2019 ieee.

关键词： Maintenance

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Modeling the Performance of Scientific Workflow Executions on HPC Platforms with Burst Buffers

Modeling the Performance of Scientific Workflow Executions o...

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ieee International conference on Cluster Computing

作者： Loïc Pottier Rafael Ferreira da Silva Henri Casanova Ewa Deelman USC Information Sciences Institute Marina Del Rey CA USA University of Hawai'i at Manoa Honolulu HI USA

ISBN: (数字)9781728166773

ISBN: (纸本)9781728166780

Scientific domains ranging from bioinformatics to astronomy and earth science rely on traditional high-performance computing (HPC) codes, often encapsulated in scientific workflows. In contrast to traditional HPC codes that employ a few programming and runtime approaches that are highly optimized for HPC platforms, scientific workflows are not necessarily optimized for these platforms. As an effort to reduce the gap between compute and I/O performance, HPC platforms have adopted intermediate storage layers known as burst buffers. A burst buffer (BB) is a fast storage layer positioned between the global parallel file system and the compute nodes. Two designs currently exist: (i) shared, where the BBs are located on dedicated nodes; and (ii) on-node, in which each compute node embeds a private BB. In this paper, using accurate simulations and realworld experiments, we study how to best use these new storage layers when executing scientific workflows. These applications are not necessarily optimized to run on HPC systems, and thus can exhibit I/O patterns that differ from that of HPC codes. Thus, we first characterize the I/O behaviors of a real-world workflow under different configuration scenarios on two leadership-class HPC systems (Cori at NERSC and Summit at ORNL). Then, we use these characterizations to calibrate a simulator for workflow executions on HPC systems featuring shared and private BBs. Last, we evaluate our approach against a large I/O-intensive workflow, and we provide insights on the performance levels and the potential limitations of these two BBs architectures.

关键词： Runtime Buffer storage Interference Geoscience programming open source software Next generation networking

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NCSbench: Reproducible Benchmarking Platform for networked Control Systems

NCSbench: Reproducible Benchmarking Platform for Networked C...

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Consumer Communications and networking conference, CCNC ieee

作者： Samuele Zoppi Onur Ayan Fabio Molinari Zenit Music Sebastian Gallenmüller Georg Carle Wolfgang Kellerer Chair of Communication Networks TUM Germany Control Systems Group TU Berlin Germany Chair of Network Architectures and Services TUM Germany

ISBN: (数字)9781728138930

ISBN: (纸本)9781728138947

The evolution of the Internet of Things accelerated the development of Cyber-Physical Systems. Among them, networked Control Systems (NCS) gained notable attention thanks to their application to industrial operations. Experimental NCS require expertise from control, computation, and communication disciplines. This requirement, together with the fragmentation of implementation platforms and experimental investigations, represents a challenge for the reproducibility and comparison of research results. In this paper, we tackle this problem by proposing a novel NCS benchmarking methodology that aids the reproducibility of NCS experiments. Relying on a novel approach to model the architectural elements and the delays of NCS, the methodology defines the experiment parameters and the relevant Key Performance Indicators (KPIs) that need to be observed during its execution. Furthermore, we detail the implementation of the first reproducible benchmarking platform for NCS. The proposed platform is open-source and designed to be easily reproducible and extensible by anyone. Finally, we replicate and evaluate the platform following the proposed NCS benchmarking methodology. The experimental results evaluate and compare the KPIs during the execution of the platform in different benchmarking scenarios, proving the validity of the proposed benchmarking methodology.

关键词： computer network security cyber-physical systems delays Internet of Things networked control systems networked control systems Internet of things network security Cyber-physical systems BENCHMARKS Platform delay performance indicators

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