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Single Node Acceleration of Generative Adversarial networks using HPC for Image Analytics 22

Single Node Acceleration of Generative Adversarial Networks ...

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Proceedings of the 2022 5th International conference on Computational Intelligence and Intelligent Systems

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (纸本)9781450397612

Generative Adversarial networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for neural networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.

关键词： Generative Adversarial network High-Performance Computing Graphic processing Unit Tensor processing Unit

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YinYangRAN: Resource Multiplexing in GPU-Accelerated Virtualized RANs

YinYangRAN: Resource Multiplexing in GPU-Accelerated Virtual...

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IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： Leonardo Lo Schiavo Jose A. Ayala-Romero Andres Garcia-Saavedra Marco Fiore Xavier Costa-Perez Universidad Carlos III de Madrid Spain IMDEA Networks Institute Spain NEC Laboratories Europe GmbH Germany i2CAT Foundation and ICREA Spain

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

RAN virtualization is revolutionizing the telco industry, enabling 5G distributed Units to run using general-purpose platforms equipped with Hardware Accelerators (HAs). Recently, GPUs have been proposed as HAs, hinging on their unique capability to execute 5G PHY operations efficiently while also processing Machine Learning (ML) workloads. While this ambivalence makes GPUs attractive for cost-effective deployments, we experimentally demonstrate that multiplexing 5G and ML workloads in GPUs is in fact challenging, and that using conventional GPU-sharing methods can severely disrupt 5G operations. We then introduce YinYangRAN, an innovative O-RAN-compliant solution that supervises GPU-based HAs so as to ensure reliability in the 5G processing pipeline while maximizing the throughput of concurrent ML services. YinYangRAN performs GPU resource allocation decisions via a computationally-efficient approximate dynamic programming technique, which is informed by a neural network trained on real-world measurements. Using workloads collected in real RANs, we demonstrate that YinYangRAN can achieve over 50% higher 5G processing reliability than conventional GPU sharing models with minimal impact on co-located ML workloads. To our knowledge, this is the first work identifying and addressing the complex problem of HA management in emerging GPU-accelerated vRANs, and represents a promising step towards multiplexing PHY and ML workloads in mobile networks.

关键词： Multiplexing 5G mobile communication Computer network reliability neural networks Graphics processing units Throughput Reliability

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A Predictive Real-Time Short-Term Battery Scheduling Method for Peak Load Shaving in Residential distributed Energy Storage Systems 11

A Predictive Real-Time Short-Term Battery Scheduling Method ...

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11th Smart Grid conference (SGC)

作者： Barzkar, Arash Barzkar, Ashkan Hosseini, Seyed Mohammad Hassan Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran Virginia Tech Bradley Dept ECE Blacksburg VA USA

ISBN: (纸本)9781665401654

Providing a smooth power demand in many applications is necessary so as to maintain the Grid stability. Peak load shaving is a method in demand side management (DSM) requiring information on the future load profile data and forecasting methods. In this paper, an artificial neural network shortterm load forecasting (ANNSTLF) method is adapted in order to apply forecasted load demand data to a previously discussed non-predictive real-time battery scheduling peak load shaving system. The introduced algorithm aims to provide peak load shaving and load smoothing in residential areas and can be considered for small processing systems. The ANNSTLF is implemented in a non-predictive peak load shaving algorithm, where uses profile data of a residual for a year to train the artificial neural network (ANN) to forecast the day ahead load demand. In order to assess the performance of the proposed method, simulations in MATLAB are done and predictive and the non-predictive approaches results are compared to each other.

关键词： Artificial neural network (ANN) battery energy storage system (BESS) peak load shaving short-term load forecasting (STLF)

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distributed neural network-Based DDoS Detection in Vehicular Communication Systems

Distributed Neural Network-Based DDoS Detection in Vehicular...

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International conference on Signal processing and Communication Systems, ICSPCS

作者： Nicholas Jaton Sohan Gyawali Yi Qian Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE USA Department of Technology Systems East Carolina University Greenville NC USA

As modern vehicular communication systems advance, the demand for robust security measures becomes increasingly critical. A misbehavior detection systems (MDS) is a tool developed to detect if a vehicular network is being attacked so that the system can take steps to mitigate harm from the attacker. Vehicular communication systems face significant risks from distributed denial of service (DDoS) attacks. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this paper, we first survey the current MDS literature and how it is used to detect and mitigate DDoS attacks. We then propose a new distributed multilayer perceptron classifier (MLPC) for DDoS detection and evaluate the performance of the proposed detection scheme in vehicular communication systems. For the evaluations using simulations, two specific implementations of the attacks are conducted. Apache Spark is then used to create the distributed MLPC. The median F1-score for this MLPC method was 95%. The proposed method outperformed linear regression and support vector machines, which achieved 89% and 88% respectively, but is unable to perform better than random forests and gradient boosted trees which both achieved a 97% F1-score. Using Amazon Web Services (AWS), it is determined that model training and detection time are not significantly increased with the inclusion of additional nodes after three nodes including the master.

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Reconfigurable and Green FPGA Accelerator Design for Deep neural networks on IIoT Devices

Reconfigurable and Green FPGA Accelerator Design for Deep Ne...

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IEEE conference on Global Communications (GLOBECOM)

作者： Mohsen Pourghasemian Martin Lastovka Firooz B. Saghezchi Haris Gacanin Chair for Distributed Signal Processing RWTH Aachen University Aachen Germany

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

We propose an extremely energy efficient and reconfigurable accelerator for performing Deep neural network (DNN) inferences on a Field-Programmable Gate Array (FPGA). Our design allows on the fly reconfigurability of the model to adapt it to any arbitrary DNN inference task and perform it with an extremely low latency, on the scale of tens of micro seconds. Our design can be adopted by resource constrained Industrial Internet of Things (IIoT) devices (e.g., mobile robots) to fulfill different DNN inference tasks. By parallelization of the matrix multiplications in each layer of the DNN and adopting an adjustable sliding window at the synthesize stage, our accelerator strikes a balance between the computing latency and the scarce resource utilization on the FPGA. It also uses a low-overhead control scheme for the accelerator’s internal interactions to update the DNN model on-the-fly. The results show that for a DNN model with 64 input features, 9 hidden layers (each with 64 neurons), and 64 output variables, our proposed inference accelerator speeds up the inference by 28.8 times and decreases the energy consumption by 31 % compared to an AMD Deep Learning processing Unit.

关键词： Wireless communication Power demand Computational modeling Artificial neural networks Energy efficiency Mobile robots Resource management Low latency communication Field programmable gate arrays Industrial Internet of Things

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End-to-End Training for Acoustic Scene Analysis with distributed Sound-to-Light Conversion Devices 29

End-to-End Training for Acoustic Scene Analysis with Distrib...

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29th European Signal processing conference (EUSIPCO)

作者： Kinoshita, Yuma Ono, Nobutaka Tokyo Metropolitan Univ Tokyo Japan

ISBN: (纸本)9789082797060

We propose an end-to-end acoustic scene analysis framework with distributed sound-to-light conversion devices called Blinkies. Blinkies transmit sound information as the intensity of an on-board light-emitting diode (LED). A video camera can then easily collect acoustic information by capturing the LED intensities from multiple Blinkies distributed over a large area. However, the transmitted signal is band-limited owing to a video camera's frame rate, typically 30 frames per second. We aim to optimize the sound-to-light conversion process for acoustic scene analysis under this bandwidth constraint. In lightsignal propagation in air, signal degradation due to physical constraints such as light attenuation and noise will also occur. We model the physical constraints as differentiable physical layers, which enable us to train two deep neural networks (DNNs) for sound-to-light conversion and acoustic scene analysis in an end-to-end manner. Simulation experiments of acoustic scene analysis using a DCASE 2018 dataset show that the proposed framework can produce a higher accuracy than the previous framework with Blinkies This result suggests the suitability of Blinkies for acoustic scene analysis.

关键词： Blinky Sound-to-light conversion Deep learning Differentiable physical layer

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Byzantine-Robust and Communication-Efficient Personalized Federated Learning

Byzantine-Robust and Communication-Efficient Personalized Fe...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xuechao He Jiaojiao Zhang Qing Ling Sun Yat-Sen University KTH Royal Institute of Technology

This paper investigates personalized federated learning, in which a group of workers are coordinated by a server to train correlated local models, in addition to a common global model. This distributed statistical learning problem faces two challenges: efficiency of information exchange between the workers and the server, and robustness to potential malicious messages from the so-called Byzantine workers. We propose a projected stochastic block gradient descent method to address the robustness issue. Therein, each regular worker learns in a personalized manner with the aid of the global model, and the server judiciously aggregates the local models via a Huber function-based descent step. To improve communication efficiency, we allow the regular workers to perform multi-steps of local update per communication round. Convergence of the proposed method is established for non-convex personalized federated learning. Numerical experiments on neural network training validate advantages of the proposed method over the existing ones.

关键词： Training Federated learning Statistical learning neural networks Stochastic processes Signal processing Robustness

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Securing Critical Infrastructure Through Innovative Use Of Merged Hierarchical Deep neural networks 18

Securing Critical Infrastructure Through Innovative Use Of M...

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18th International conference on Privacy, Security, and Trust (PST)

作者： Gupta, Lay Univ Missouri Dept Comp Sci St Louis MO 63121 USA

ISBN: (纸本)9781665401845

Multi-clouds are becoming central to large and modern applications including those in business, industry and critical infrastructure sectors. Designers usually deploy these clouds hierarchically to get the best advantage of low latency of the edge clouds and high processing capabilities of the core clouds. The data that flows into the clouds for processing and storage and moves out to other system domains for further use must cross multiple trust boundaries, and as a result, face large attack surfaces. This gives malicious actors abundant opportunity to penetrate and potentially harm organizations or bring down critical services causing widespread disruptions and mayhem. Deep neural network models can be used in innovative ways to protect the confidentiality and integrity of dataflows in the clouds. However, the use of deep learning comes with some challenges. In large multi-location and multi-cloud environments, deep learning models grow rapidly in size and complexity, inhibiting fast training of cloud models and making it difficult to maintain accuracy of detection of known and unknown attacks on the datain-motion. This impedes their use in critical infrastructure services. We propose innovative distributed-hierarchical-merged models, which make use of cooperative training at the edge and the core clouds, and the power of data and model parallelisms, to achieve rapid training with high accuracy. Our broad objectives in this paper are twofold: Firstly, we show that merged hierarchical deep learning models, working cooperatively in the multi-cloud, significantly reduce the parameters to be trained and results in faster core cloud training time. Secondly, training the merged core model with distribute strategy for data parallelism on CPUs and GPUs further reduces the training time significantly. We also show that while achieving improvement by about 25% over unmerged models and the accuracy of detection of unknown attacks in the range 96.9-99.5%.

关键词： performance multi-cloud edge clouds core clouds deep neural networks stacked sparse autoencoders hierarchical models distributed models data parallelism model parallelism

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Internet of Things Enabled Smart Energy Management of distributed Energy Resources using Self-Adaptive Physics-Informed neural network Optimized with Green Anaconda Optimization

Internet of Things Enabled Smart Energy Management of Distri...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Rahul D. Ghongade J. Sri Arunaa P. S. Latha Mageshwari K. Vinoth M. Shanmugathai J. Femila Mercy Rani Department of Electronics and Telecommunication Engineering P. R. Pote Patil College of Engineering and Management Amravati Maharashtra India Department of Electronics and Communication Engineering Dr. M. G. R Educational and Research Institute Chennai Tamil Nadu India Department of S&H (Physics) R. M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of English Sri Sairam Engineering College Chennai Tamil Nadu India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Internet of Things (IoT)-enabled Smart Energy Management (SEM) in distributed Energy Resources (DERs), while crucial for optimizing energy distribution and resource management faces challenges such as data inconsistencies and the variability of renewable energy generation. These issues result in inaccurate demand forecasts, leading to suboptimal allocation of resources and inefficient Energy Management (EM). Additionally, as energy demand and generation patterns are influenced by factors like weather conditions, time of day, and energy consumption behaviors, prediction models may struggle to capture these dynamic changes, affecting the reliability of forecasts. To address these limitations, this manuscript proposes a novel approach for energy demand prediction. Data is collected from IoT-enabled sensors monitoring DERs. The data undergoes pre-processing, where the Fast Resampled Iterative Filtering (FRIF) method is used to eliminate missing values and normalize the inputs. The Self-Adaptive Physics-Informed neural network (SAPINN) model then utilizes the processed data to forecast energy demand, renewable energy generation, and storage levels. Green Anaconda Optimization (GAO) is applied to optimize the weight parameters of the SAPINN model. The proposed SAPINN-GAO method is implemented using the MATLAB platform and compared with existing models, such Stacked Convoluted Bi-Directional Gated Attention network-Hybrid Darts Seagull Optimizer (SConBGAN-HDSO), Recurrent neural network (RNN), and Support Vector Machine-Particle Swarm Optimization (SVM-PSO). The SAPINN-GAO method achieves an accuracy of 99.2%, precision of 99.2%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in energy demand prediction. The SAPINN-GAO method's higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for energy demand, renewable energy generation, and storage levels forecasting in IoT-enabled SEM syst

关键词： Energy consumption Renewable energy sources Accuracy Recurrent neural networks Green products Predictive models Internet of Things Reliability Energy management Optimization

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MAPPING THE TIMESCALE ORGANIZATION OF neural LANGUAGE MODELS 9

MAPPING THE TIMESCALE ORGANIZATION OF NEURAL LANGUAGE MODELS

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9th International conference on Learning Representations, ICLR 2021

作者： Chien, Hsiang-Yun Sherry Zhang, Jinhan Honey, Christopher J. Department of Psychological and Brain Sciences Johns Hopkins University BaltimoreMD21218 United States

In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual information are functionally organized. Therefore, we applied tools developed in neuroscience to map the "processing timescales" of individual units within a word-level LSTM language model. This timescale-mapping method assigned long timescales to units previously found to track long-range syntactic dependencies. Additionally, the mapping revealed a small subset of the network (less than 15% of units) with long timescales and whose function had not previously been explored. We next probed the functional organization of the network by examining the relationship between the processing timescale of units and their network connectivity. We identified two classes of long-timescale units: "controller" units composed a densely interconnected sub-network and strongly projected to the rest of the network, while "integrator" units showed the longest timescales in the network, and expressed projection profiles closer to the mean projection profile. Ablating integrator and controller units affected model performance at different positions within a sentence, suggesting distinctive functions of these two sets of units. Finally, we tested the generalization of these results to a character-level LSTM model and models with different architectures. In summary, we demonstrated a model-free technique for mapping the timescale organization in recurrent neural networks, and we applied this method to reveal the timescale and functional organization of neural language models. © 2021 ICLR 2021 - 9th International conference on Learning Representations. All rights reserved.

关键词： Mapping

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