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检索条件"机构=Laboratory of Embedded and Distributed Systems"

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TinyML Applied in Hyperspectral Image Classification on COTS Microcontroller 14

TinyML Applied in Hyperspectral Image Classification on COTS...

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14th Brazilian Symposium on Computing systems Engineering, SBESC 2024

作者： Hutner, Joao Victor Santos Viel, Felipe Zeferino, Cesar A. Bezerra, Eduardo Augusto University of Vale do Itajaí - UNIVALI Laboratory of Embedded and Distributed Systems - LEDS SC Itajaí Brazil Federal University of Santa Catarina - UFSC SpaceLab SC Florianópolis Brazil

ISBN: (纸本)9798331509002

Commercial off-the-shelf (COTS) microcontrollers used in remote sensing are increasingly being used because of the great versatility of application and development provided by this type of component. There has been a significant increase in missions using CubeSats, mainly due to the greater accessibility and flexibility of the COTS equipment used, as well as lower development costs related to this type of equipment. However, CubeSats have main constraints in power, size, and data transmission capacity. As a solution, numerous works use onboard data processing to compress or classify the captured data to reduce the use of storage memory and the amount of data sent to Earth stations. One of the types of data collected by remote sensing satellites are hyperspectral images, which despite being computationally challenging for classification in microcontrollers. Several algorithms are capable of doing hyperspectral image classification even that constrained by low-power microcontrollers with the advances in deep learning. In this sense, the aim and contribution of this study was to demonstrate and explore the feasibility and performance of implementing a deep learning model on a COTS microcontroller for hyperspectral image classification. We developed a convolutional neural network (CNN) architecture adapted for microcontrollers using the TinyML concept with the TensorFlow Lite library to demonstrate the feasibility and performance of deep learning in COTS microcontroller. The results for the overall accuracy of the embedded model were from 82.57 % to 95.81 % for three different literature datasets. The dissipated power was 105 mW for all dataset samples, with the execution time per sample varying from 12.14 ms to 93.18 ms, and the total energy consumed varying from 4.09 mJ to 7.52 mJ for the entire dataset tested. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Nibble: A 4-bit Number Format for Efficient DNN Training and Inference in FPGA

Deep Nibble: A 4-bit Number Format for Efficient DNN Trainin...

引用

Symposium on Integrated Circuits and systems Design (SBCCI)

作者： Luiz F. H. Duarte George B. Nardes Wesley Grignani Douglas R. Melo Cesar A. Zeferino Laboratory of Embedded and Distributed Systems University of Vale do Itajaí Brazil Webphy Systems Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

This paper introduces a compact number format (Deep Nibble) and a resource- and performance-efficient dot product core (Deep Nibble Unit – DNU) designed to address the performance and memory bottlenecks of deep learning on resource-constrained devices. Deep Nibbles are 4-bit signal-exponent numbers generated by the proposed normalized stochastic logarithmic quantization (NSLQ) method. This method mitigates the gradient vanishing and exploding problems in training deep neural networks (DNNs) using narrow data types. DNU is a Deep Nibble-based core designed to accelerate MAC operations of forward and backward passes of deep learning systems. Compared to FP32 formats, the experimental results show that Deep Nibble supports the training of DNN models with an acceptable accuracy degradation while reducing the memory footprint by 8×. Compared to state-of-the-art, the FPGA implementation of the DNU reduces the area per MAC and increases the throughput per area by 3× and 2.3×, respectively.

关键词： Deep learning Training Performance evaluation Accuracy Quantization (signal) Memory management Stochastic processes Artificial neural networks Throughput Integrated circuit modeling

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Reliability analysis in ARM *** heterogeneous multi-core processor for IoT

Reliability analysis in ARM *** heterogeneous multi-core pro...

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International Conference on Internet of Things and Applications (IoT)

作者： Mahin Moradiyan Yaser Sedaghat Computer Engineering Department Dependable Distributed Embedded Systems (DDEmS) Laboratory Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

This study navigates the landscape of the Internet of Things (IoT), spotlighting the escalating demand for robust processing solutions, thereby propelling the pervasive adoption of heterogeneous multi-core processors. Within this sphere, the ARM *** architecture takes center stage, adeptly harmonizing high performance with energy efficiency. The investigation zooms in on the pivotal role of failure-sensitive applications in the IoT realm, critical domains like healthcare. The paper underscores the indispensable need for failure analysis concerning ARM *** processors, unravel the complexities of data flow errors (DFE) and control flow errors (CFE). By providing insights into potential pitfalls and strategies for fortifying reliability, the study contributes to the evolving discourse on the resilience of ARM *** processors in the context of failure-sensitive IoT applications. Also concludes by emphasizing the need for continued exploration of optimal task mapping and fault tolerance strategies, paving the way for future research to refine and extend the reliability analysis of ARM *** platforms in evolving IoT landscapes.

关键词： Program processors Multicore processing Smart cities Navigation Fault tolerant systems Medical services Propulsion

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TinyML Applied in Hyperspectral Image Classification on COTS Microcontroller

TinyML Applied in Hyperspectral Image Classification on COTS...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： João Victor Santos Hütner Felipe Viel Cesar A. Zeferino Eduardo Augusto Bezerra Laboratory of Embedded and Distributed Systems - LEDS University of Vale do Itajaí - UNIVALI Itajaí SC Brazil SpaceLab Federal University of Santa Catarina - UFSC Florianópolis SC Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

关键词： Deep learning Adaptation models Satellites Microcontrollers Tiny machine learning Computational modeling Convolutional neural networks CubeSat Hyperspectral imaging Image classification

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Improving Fault Tolerance of LoRaWAN With Predicting Packet Collision

Improving Fault Tolerance of LoRaWAN With Predicting Packet ...

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International CSI Computer Conference, CSICC

作者： Reza Rezazadeh Yasser Sedaahat Ismail Ghodsollahee Farzad Azizi Department of Computer Engineering Dependable Distributed Embedded Systems (DDEms) Laboratory Ferdowsi University of Mashhad Mashhad Iran Department of Computer Engineering Amirkabir University of Technology Tehran Iran

the dependability of IIoT functionality is strongly influenced by its communication protocols. One of the most popular communication standards for the Internet of Things, whose features make it attractive in the IIoT field, is LoRaWAN. LoRaWAN can be considered a low-consumption competitor to cellular networks. This protocol has important features such as long-range coverage, low energy consumption, and a low implementation cost. Although the LoRaWAnfeatures make it a good choice for IoT applications, the existence of real-time constraints and a harsh environment in the IIoT field make it a challenge for network designers to guarantee the dependability of IIoT networks with LoRaWAN. Therefore, in this paper, to overcome this challenge, a novel method is presented for improving energy consumption efficiency, latency, and fault tolerance of the LoRaWAN protocol. The evaluation results show that the proposed method improves packet delivery and latency rate by 11% and 21% compared to standard LoRaWAn.

关键词： Energy consumption Fault tolerance Protocols Costs Fault tolerant systems Packet loss Real-time systems

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EHR Data Persistence for a Nationwide Shared Medical Record: An Overview

EHR Data Persistence for a Nationwide Shared Medical Record:...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Latifa OUFKIR Asma EL HAMZAOUI Laboratory of Research in Information Technology and Telecommunication (LRIT) Rabat IT Center Faculty of Sciences Mohammed V University Rabat Morocco Smart Embedded Enterprise and Distributed Systems (SEEDS) Institut National des Postes et Télécommunications Rabat Morocco

ISBN: (数字)9798350377866

ISBN: (纸本)9798350377873

Electronic health records (EHR) are considered the backbone of modern health system. When they are implemented effectively at scale, they hold the promise of enhancing healthcare services' quality and efficiency. However, EHR implementations vary based on factors such as EHR usage scenarios and technological capacities, resulting in varying volumes, complexity, and types of healthcare data. Consequently, the data storage model and query capabilities must align with these variations to achieve desired outcomes. This paper reviews the standardized EHR data persistence based on existing EHR literature and discusses its appropriateness for current healthcare usage scenarios including nationwide access. Insights into relevant literature reveal that NoSQL databases are more appropriate for current EHR usage scenarios. However, choosing among NoSQL databases’ persistence depends on the use case it is intended for in addition to the standards used for data integration between systems. Accordingly, clinical usage scenarios are best served with document stores for standardized EHR following the dual model. For population-based queries, JSON stores provide a good balance between storage capacity, query performance, and flexibility. The graph-based model presents a good solution for nationwide access considering the workload and the interconnected healthcare data nature and structure. One way to respond to the conflicting data persistence requirements is the multi-model database (MMDB) concept in both its forms: polyglot and multi-model databases.

关键词： Runtime Databases Reviews Wireless networks NoSQL databases Medical services Mobile communication

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Evaluating the CCSDS 123 Compressor Running on RISC-V and ARM Architectures 10

Evaluating the CCSDS 123 Compressor Running on RISC-V and AR...

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10th Brazilian Symposium on Computing systems Engineering, SBESC 2020

作者： Imianosky, Carolina Valim, Paulo R. O. Zeferino, Cesar A. Viel, Felipe Laboratory of Embedded and Distributed Systems-LEDS University of Vale Do Itajaí-UNIVALI ItajaíSC Brazil

ISBN: (纸本)9781728182865

Hyperspectral images are widely used in spatial applications and are three-dimensional data structures in which the x and y axes contain spatial information, and the z-Axis contains the spectral bands, which that can reach the order of hundreds of layers. Such images generate a large amount of data, and applying compression algorithms, such as the CCSDS 123 compressor, is highly necessary to deal with the communication and storage constraints of the platforms used to capture hyperspectral images (e.g., a spacecraft). Given that the CCSDS 123 algorithm has a high computational cost, it is necessary to evaluate which processor architectures can deal with it onboard. Given the context above, this work presents an evaluation of performance and power consumption of the CCSDS 123 algorithm running on RISC-V and ARM processors and an evaluation of using two real-Time operating systems (FreeRTOS and Zephyr). The experimental results show that, for the development kits used, the RISC-V processor dissipates less power than the ARM processor, which, in turn, offers much higher performance and lower energy consumption than RISC-V. Results also show that FreeRTOS adds a lower overhead to the algorithm execution in comparison to Zephyr when running over the RISC-V processor. © 2020 IEEE.

关键词： Hyperspectral imaging

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Cloud Telescope: A distributed architecture for capturing Internet Background Radiation

Cloud Telescope: A distributed architecture for capturing In...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Fabricio Bortoluzzi Barry Irwin Lucas Silvero Beiler Carla Merkle Westphall Cyber Security Programme Noroff University College Kristiansand Norway Postgraduate Programme in Computer Science University of Santa Catarina Florianópolis Brazil Department of Computer Science Rhodes University Grahamstown South Africa Laboratory of Embedded & Distributed Systems University of Vale do Itajai Itajai Brazil

Internet Background Radiation (IBR) comprises a range of unsolicited traffic directed towards Internet hosts. In general, this type of traffic is characterised by high levels of port scanning activity, malware propagation, application exploits, system misconfiguration and denial-of-service attacks. IBR capture is typically undertaken by a system termed a network telescope. This records unfiltered incoming internet traffic for a specific CIDR block in the form of a packet capture (PCAP) file for analysis. This work proposes a novel, cloud-native approach to capturing IBR by the deployment of an ephemeral and reproducible architecture, described as code, distributed across all regions of a cloud service provider. In this paper we discuss the technical and financial viability of using a fleet of small-sized compute instances, in a spot price auction model, to maximise platform collection, capillarity and duration. We also present an overview analysis of the primary characteristics of IBR as collected during a month long proof-of-concept experiment across 26 regions of a cloud service provider in May 2023. Our analysis discusses the aspects of the dataset in quantitative terms: traffic aggregation per protocol, top TCP and UDP ports, top radiation sources and radiation distribution per cloud region. We also provide an overview of the most relevant threats detected. Our results include a formalisation and validation of the cloud telescope, with the corresponding supporting architecture described in Terraform and Ansible. The aggregate dataset amounted to 2.2 GB, and 21.8 million packets. Composition by protocol was 78% TCP, 14% ICMP and 8% UDP.

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Improving the Fault Tolerance and Efficiency of CAN Communication Networks Based on Bus Redundancy

Improving the Fault Tolerance and Efficiency of CAN Communic...

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International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Mohamad Reza Pourmoghadam Yasser Sedaghat Ismail Ghodsollahee Dependable Distributed Embedded Systems (DDEmS) Laboratory Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9781728185668

ISBN: (纸本)9781728185675

distributed embedded systems which are employed in many applications (e.g., automotive, avionics, military, and industrial control systems), generally consist of several processing units that are connected to each other through a communication network. Some of these applications are safety-critical and real-time, therefore, a system or network failure in these applications can result in catastrophic consequences for humans or the environment. This means that in these applications, systems should perform their tasks correctly and moreover, should meet the specified deadlines. Consequently, in a safety-critical real-time distributed embedded system, fault tolerance and network efficiency improvement should be considered, simultaneously. One of the most common communication protocols employed in distributed embedded systems is the Controller Area Network (CAN) communication protocol. Although this communication protocol has a maximum bit rate of 1Mbps and is based on broadcasting on a bus, it has some inherent limitations. In this paper, a technique is proposed which employs bus redundancy and a scheduling algorithm to improve the fault tolerance and the efficiency in CAN communication networks. To evaluate the proposed technique, a CAN-based distributed embedded system composed of four nodes (using ARM microcontrollers, i.e. STM32F4 and STM32F2) has been implemented. The proposed technique has been compared with the other related techniques in CAN network protocol. To perform this comparison, four experiments have been performed in two forms of fault-free and faulty buses. The evaluation results show that the proposed technique improves throughput, response time, and energy consumption parameters compared to a standard CAN, 29%, 37% and 19%, and compared to a redundancy-based technique, 77%, 41% and 21%, respectively.

关键词： Protocols Redundancy embedded systems Real-time systems Fault tolerant systems Media Communication networks

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Improving the Security and Reliability of embedded Networks With TTMAC-CAN

Improving the Security and Reliability of Embedded Networks ...

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International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Ismail Ghodsollahee Yasser Sedaghat Mohamad Reza Pourmoghadam Deppendable Distributed Embedded Systems (DDEms) Laboratory Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9781728185668

ISBN: (纸本)9781728185675

The widespread use of distributed embedded systems to monitor and control large-scale industrial facilities (e.g., power plants, and electrical power grids), and the growing development of Internet of Things (IoT) have led to Industrial IoT (IIoT). In a typical IIoT, distributed embedded systems, composed of processors, sensors and actuators which are connected through embedded network protocols, are connected to the internet for remote management. However, embedded networks and therefore industrial facilities are very vulnerable to cyber-attacks. In this paper TTMAC-CAN, a novel scheduling algorithm is proposed which utilizes a multi-objective evolutionary algorithm to improve security, reliability, and performance of Time Triggered Controller Area Network (TTCAN) protocol. The proposed technique in comparison with TOUCAN reduces the risk of guessing the tag by about 86% and improve receiving cycle time by 98%.

关键词： Security Reliability Protocols Throughput Authentication Real-time systems Microcontrollers

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