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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing III"

共 250 条记录，以下是31-40 订阅

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Design-Space Exploration of Systolic Array for Edge Inferencing Applications 23

Design-Space Exploration of Systolic Array for Edge Inferenc...

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Proceedings of the Third International conference on AI-ML systems

作者： Prashanth H C Yash Dharmesh Mogal Madhav Rao International Institute of Information Technology Bangalore IN International Institute of information Technology Bangalore IN

ISBN: (纸本)9798400716492

Systolic array (SA) architectures are suitable hardware accelerators to run artificial intelligence (AI) algorithms due to their scalable and pipelined structure. Inferencing on edge devices enables decision-making capability in a small, power-efficient form factor suitable for many IoT-based applications. Convolutional neural networks (CNNs), which form a major part of AI algorithms, are generally memory and compute-intensive algorithms. Optimizing resource-constrained SA-based edge-computing system architecture is crucial. The primary challenge is to find an optimal SA configuration with sufficient on-chip and off-chip memory, processing elements, and optimal memory-bandwidth interface for achieving minimum latency and stall cycles while maximizing compute utilization and mapping efficiency. This work characterizes the popular ResNet50 CNN model on different SA architecture configurations that are structured using a realistic designer tool SCALE-SIM. The design-space exploration of SA was carried out to run the Resnet50 model by configuring i) array sizes (processing elements matrix), ii) SRAM memory sizes available to the computing elements, iii) varying interface bandwidth of memory to the computing elements. The compute latency in clock cycles, SA utilization, Mapping efficiency, number of Stall cycles, and SRAM and DRAM bandwidth requirements were extracted using cycle accurate runs. All three types of data-flow models, including output stationary (OS), weight stationary (WS), and input stationary (IS), were investigated in this work. Unlike previous research that relied on benchmarking various mathematical models, this study focuses on characterizing SA configurations using the ResNet50 model. It presents comprehensive results for all the investigated configurations and establishes the most efficient SA architecture for running CNN architectures. Moreover, it provides valuable insights for designing resource-constrained edge-inferencing systems.

关键词： AI Accelerators CNN Design Space Exploration Systolic Array

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Case-Study for Integration of COTS SoC devices in Reliable Space systems for On-Board processing

Case-Study for Integration of COTS SoC Devices in Reliable S...

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Data Handling & Data processing conference (EDHPC), European

作者： Ivan Rodriguez-Ferrandez David Steenari Maris Tali Leonidas Kosmidis Ferdinando Tonicello Universitat Politècnica de Catalunya (UPC) Barcelona Spain Barcelona Supercomputing Center (BSC) Barcelona Spain European Space Agency (ESA) Noordwijk The Netherlands

Recent trends in Data Handling systems (DHS) include increased data-rates, in-orbit reconfiguration and the introduction of advanced On-Board processing (OBP) methods to extract actionable information on-board, with low latency. At the same time, NewSpace industry has successfully deployed COTS-based DHS and processing equipment in space. In particular for OBP, COTS-based processors and FPGAs can offer higher computational performances than space qualified equivalents – which in turn can enable new applications through more advanced OBP algorithms. The individual component cost is also lower for COTS than space qualified components, which can allow overall cost-optimisations on mission *** internal working group at ESA has studied the concept of using "safety barriers" to ensure no propagation of failure from functions implemented with COTS to other on-board units. Thanks to ESA studies there is a path to use Class IV equipment on missions with higher class (Class I-iii), through the use of hardware "safety barriers" to limit failure propagation from Commercial Off-The-Shelf (COTS) *** our work, we take the above mentioned concepts and apply them to reference implementations, targeting payload processing modules based on standard form factors (ADHA) and automotive-grade GPU systems-on-Chip (SoCs). In addition, we make use of previous work on improving in-flight availability of complex COTS SoC processors, through system and software Fault Detection, Isolation and Recovery (FDIR) techniques.

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RTT Algorithm Optimization with Out-of-Order Retransmission Detection

RTT Algorithm Optimization with Out-of-Order Retransmission ...

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International conference on Wavelet Active Media Technology and information processing (ICWAMTIP)

作者： Jiang Wei Zhang Bin Wang Wenyong Zhu Qixun School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China China Telecom Sichuan Branch Chengdu Sichuan China International Institute of Next Generation Internet Macau University of Science and Technology Macau Sichuan Communication Research Planning & Designing Co. Ltd

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Traditional Round-Trip Time (RTT) algorithms often struggle with maintaining accuracy under conditions of packet retransmissions and disorder, resulting in RTT estimations that fall outside the expected range. This can lead to misinterpretations of network health and performance. In response to this challenge, our research introduces an enhanced RTT calculation method designed for implementation within network devices. Our approach involves a bidirectional monitoring mechanism for detecting packet retransmissions during the process of traffic interception. By doing so, we aim to refine the precision of RTT estimations through meticulous packet inspection. Our novel algorithm is rigorously tested under simulated environments that mimic fluctuating network conditions. The outcomes clearly indicate that our methodology successfully screens out anomalous RTT values, which are typically introduced by packet retransmissions or disorder. Consequently, this leads to a substantial enhancement in the accuracy of RTT calculations. Our findings suggest that the adoption of this optimized RTT algorithm could significantly improve the reliability of network performance monitoring systems, making it a promising solution for real-world network management applications. The comparative experimental results of RTT optimization before and after simulating abnormal network environments show that the peak value of sudden abnormal RTT over the family side and the network side after optimization is reduced by about 75.8% and 99.5%, respectively.

关键词： Out of order Accuracy Refining Estimation Market research optical network units Real-time systems Reliability Optimization Monitoring

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Artificial Neural Network Training on an optical Processor via Direct Feedback Alignment

Artificial Neural Network Training on an Optical Processor v...

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conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Kilian Müller Julien Launay Iacopo Poli Matthew Filipovich Alessandro Capelli Daniel Hesslow Igor Carron Laurent Daudet Florent Krzakala Sylvain Gigan LightOn Paris France École Polytechnique Fédérale de Lausanne (EPFL) Information Learning and Physics lab. Lausanne Switzerland Laboratoire Kastler Brossel ENS-Universite PSL CNRS Sorbonne Universite College de France Paris France

ISBN: (纸本)9798350345995

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring results. However, there are good reasons to look for alternative training methods: With current algorithms and hardware constraints sometimes only half the available computing power is actually used. This is due to a complicated interplay between the size of the ANN, the available memory, throughput limitations of interconnects, the architecture of the network of computers, and the training algorithm. Training a model like the aforementioned GPT-3 takes months and costs millions. A different training paradigm, which could make clever use of specialized hardware, may train large ANNs more efficiently.

关键词： Neural networks optical neural systems optical signal processing devices

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New reconstruction methodology for thorax tomosynthesis based on deep learning 7

New reconstruction methodology for thorax tomosynthesis base...

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7th International conference on Image Formation in X-Ray Computed Tomography

作者： F Del Cerro, C. Galán, A. García Blas, J. Desco, M. Abella, M. The Dpto. Bioingeniería e Ingeniería Aeroespacial Universidad Carlos III de Madrid The Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain The Dpto. Arquitectura de Computadores Comunicaciones y Sistemas Universidad Carlos III de Madrid Madrid Spain Madrid Spain Madrid Spain

ISBN: (纸本)9781510656697

Tomosynthesis offers an alternative to planar radiography providing pseudo-tomographic information at a much lower radiation dose than CT. The fact that it cannot convey information about the density poses a major limitation towards the use of tomosynthesis in chest imaging, due to the wide range of pathologies that present an increase in the density of the pulmonary parenchyma. Previous works have attempted to improve image quality through enhanced analytical, iterative algorithms, or including a deep learning-based step in the reconstruction, but the results shown are still far from the quantitative information of a CT. In this work, we propose a reconstruction methodology consisting of a filtered back-projection step followed by post-processing based on Deep Learning to obtain a tomographic image closer to CT. Preliminary results show the potential of the proposed methodology to obtain true tomographic information from tomosynthesis data, which could replace CT scans in applications where the radiation dose is critical. © 2022 SPIE.

关键词： Image enhancement

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A Substation Safety Measure Ticket Generation Algorithm based on Neural Network and Inference Engine

A Substation Safety Measure Ticket Generation Algorithm base...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Yin Wu Huicai Zhao Xingfu Huang Liqiong Huang Yuyang Hu Guangxi Power Grid Power Dispatching and Control Center Nanning China Guangxi Power Grid Limited Liability Company Nanning Power Supply Bureau Nanning China Guangxi Power Grid Limited Liability Company Guilin Power Supply Bureau Guilin China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Intelligent substations use fiber optic as the information transmission medium, transforming the interconnection relationship between secondary devices into fiber optic connections. The corresponding secondary circuits and hardware platforms are replaced with virtual circuits and software templates, resulting in a significant change in maintenance work methods and higher requirements for operators' maintenance work. This article proposes an intelligent substation safety measure ticket generation algorithm based on multi-scale feature convolutional neural network (MSF-CNN) and inference machine. Firstly, a neural network model suitable for safety ticket generation was designed and trained. Combining the structural characteristics of DenseNet, multi-scale feature extraction and logical relationship modeling were achieved through multi-layer convolution, concatenation, and pooling processing. The model uses hyperbolic tangent function as the activation function to improve the accuracy of regression prediction. Secondly, a custom word vector and equipment association modeling method based on SCD file parsing are proposed for the electricity proprietary terms and multi item logical relationships involved in the safety measures ticket text, in order to improve data expression accuracy and model understanding ability. In addition, based on the functional correlation and input-output interaction characteristics of substation equipment, a three-layer neural network-based inference engine was constructed to generate maintenance safety measures for primary and secondary equipment. The experimental results showed that the MSF-CNN model performed the best in accuracy, recall, and F1 score, with an accuracy of 92.5%, recall of 90.8%, and F1 score of 91.6%.

关键词： optical fibers Substations Accuracy Neural networks Training data Data models Inference algorithms Safety Maintenance Engines

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processing Power-based Task Scheduling Technique for Cloud-Fog Environment

Processing Power-based Task Scheduling Technique for Cloud-F...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Norbu Z. Lepcha Malka N. Halgamuge Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Systems and Business Analytics RMIT University Melbourne Australia

ISBN: (纸本)9781665409858

Internet of Things (IoT) applications have emerged as a critical component in improving people’s quality of life. The increasing volume of data generated by IoT devices, on the other hand, places a strain on the resources of traditional cloud data centres. This makes cloud data centres unable to meet the demands of IoT applications. Due to resource constraints and the massive connectivity of IoT devices, efficient task (incoming request) allocation is critical for service providers. We create a novel processing power-based task scheduling technique that employs a broker manager to distribute tasks and the process strategy across multiple devices. This is important in cloud technology because it enables organisations to effectively scale their processes. We use multithreading to parallelize processes and evaluate the performance of the proposed model using existing task allocation algorithms (i) round-robin, (ii) weighted round-robin, (iii) random allocation, (iv) genetic algorithm, and (v) dynamic algorithm. Our results show that when there are few tasks and one cluster has more processing capacity than the others, a weighted round-robin algorithm works well. The effectiveness of allocating based on weight shows no improvement in task completion time as task size increases. Using our proposed model, we show that the dynamic algorithm outperforms all other algorithms for task scheduling by 1.37 times for all task sizes.

关键词： Performance evaluation Cloud computing Data centers Job shop scheduling Heuristic algorithms Clustering algorithms Dynamic scheduling

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Locally-Adaptive Nonparametric Online Learning 34

Locally-Adaptive Nonparametric Online Learning

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34th conference on Neural information processing systems (NeurIPS)

作者： Kuzborskij, Ilja Cesa-Bianchi, Nicolo DeepMind London England Univ Milan Dept Comp Sci Milan Italy Univ Milan DSRC Milan Italy

ISBN: (纸本)9781713829546

One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is measured against rich classes of comparator functions that are able to fit complex environments. Although such hard comparators and complex environments may exhibit local regularities, efficient algorithms, which can provably take advantage of these local patterns, are hardly known. We fill this gap by introducing efficient online algorithms (based on a single versatile master algorithm) each adapting to one of the following regularities: (i) local Lipschitzness of the competitor function, (ii) local metric dimension of the instance sequence, (iii) local performance of the predictor across different regions of the instance space. Extending previous approaches, we design algorithms that dynamically grow hierarchical "-nets on the instance space whose prunings correspond to different "locality profiles" for the problem at hand. Using a technique based on tree experts, we simultaneously and efficiently compete against all such prunings, and prove regret bounds each scaling with a quantity associated with a different type of local regularity. When competing against "simple" locality profiles, our technique delivers regret bounds that are significantly better than those proven using the previous approach. On the other hand, the time dependence of our bounds is not worse than that obtained by ignoring any local regularities.

关键词： Comparators (optical)

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Mining Alarm Association Rules in optical Transmission Networks Using a Statistical Approach

Mining Alarm Association Rules in Optical Transmission Netwo...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Miao Deng Peng Li Yu Liu Rongjing Zhu Yujie Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

Due to the interconnection between devices in the optical transmission network (OTN), when one device fails and generates alarm information, a large number of other invalid alarms are generated in OTN. How to mining association rules in alarm data is an important problem. In this paper, we investigate the association rules mining problem based on a statistical method. First, we remove the chattering alarm in preprocess to reduce data redundancy. Second, we propose a statistics-based association rule mining algorithm. We use a sliding time window to scan the alarm data, and we exploit the matrix to record the corresponding results. According to the matrix, we calculate the support and confidence of association rules. Compared with other traditional association rule mining algorithms, the proposed algorithm does not need to set the minimum support artificially, and the obtained association rules have strong causality. Through extensive experiments on syn-thetic and real OTN alarm datasets, we verify the effectiveness of our proposed algorithm and the correctness of the obtained association rules.

关键词： optical interconnections Statistical analysis Redundancy optical fiber networks Adaptive optics Data mining optical devices

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Revolutionizing waste management: harnessing the power of IoT

Revolutionizing waste management: harnessing the power of Io...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Ankita Karale Anand Singh Rajawat S. B. Goyal Computer Engineering Sandip Foundation's Sandip Institute of Research Center Nashik India School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non- recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management bas

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