Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN

Süzen, Ahmet Ali; Duman, Burhan; Şen, Betül

doi:10.1109/hora49412.2020.9152915

Cited by 164 publications

(78 citation statements)

References 13 publications

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“…These SBCs have been used widely in education, experimentation, and innovation projects. Süzen et al [9] provided a benchmark analysis study addressing this category of systems.…”

Section: A Architecture Of An Embedded Systemmentioning

confidence: 99%

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Analytical Review of Cybersecurity for Embedded Systems

Aloseel

Shaw

et al. 2021

IEEE Access

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To identify the key factors and create the landscape of cybersecurity for embedded systems (CSES), an analytical review of the existing research on CSES has been conducted. The common properties of embedded systems, such as mobility, small size, low cost, independence, and limited power consumption when compared to traditional computer systems, have caused many challenges in CSES. The conflict between cybersecurity requirements and the computing capabilities of embedded systems makes it critical to implement sophisticated security countermeasures against cyber-attacks in an embedded system with limited resources, without draining those resources. In this study, twelve factors influencing CSES have been identified: ( 1) the components; (2) the characteristics; (3) the implementation; (4) the technical domain; (5) the security requirements; (6) the security problems; (7) the connectivity protocols; (8) the attack surfaces; (9) the impact of the cyber-attacks; (10) the security challenges of the ESs; (11) the security solutions; and(12) the players (manufacturers, legislators, operators, and users). A Multiple Layers Feedback Framework of Embedded System Cybersecurity (MuLFESC) with nine layers of protection is proposed, with new metrics of risk assessment. This will enable cybersecurity practitioners to conduct an assessment of their systems with regard to twelve identified cybersecurity aspects. In MuLFESC, the feedback from the systemcomponents layer to the system-operations layer could help implement "Security by Design" in the design stage at the bottom layer. The study provides a clear landscape of CSES and, therefore, could help to find better comprehensive solutions for CSES.INDEX TERMS Characteristics of embedded system, countermeasures, embedded system, cybersecurity of embedded system, MuLFESC, risk assessment.

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“…These SBCs have been used widely in education, experimentation, and innovation projects. Süzen et al [9] provided a benchmark analysis study addressing this category of systems.…”

Section: A Architecture Of An Embedded Systemmentioning

confidence: 99%

“…Cost is one of the biggest acute factors that constrain investors. [9], [19] Physical attacks Causing unpredicted damage. [25] Reconnaissance attacks.…”

Section: Cost Sensitivitymentioning

confidence: 99%

Analytical Review of Cybersecurity for Embedded Systems

Aloseel

Shaw

et al. 2021

IEEE Access

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“…Some of the works performed explore the use of deep learning techniques on single-board computers/embedded platforms, namely, Raspberry Pi and NVIDIA Jetson series. e works justify the usage of Jetson Nano for real-time computer vision tasks for its high performance per watt and considerable high performance with a lower computational cost for lighter neural network models [56,57]. rough the experiments on Jetson Nano, real-time performance is evaluated by considering the shortcomings of the object detection algorithm on the embedded platforms.…”

Section: 8mentioning

confidence: 99%

“…Our results obtained on the power consumption of the nanodevice are comparable with the similar works implemented on image and video processing applications. Examples include power consumption for realtime prediction using two-dimensional deep CNN of around 5.57 W considering 10 k dataset in [56] and around 9.3 W in [57]. e work can be considered as an impressive example of GPU and CPU cooperation for implementing the deep learning architecture that enables highly accurate detection with lesser computational cost in a more economical way.…”

Section: 8mentioning

confidence: 99%

Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

Mohan

Pillai²,

Chai

et al. 2021

Advances in Human-Computer Interaction

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The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.

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“…W ITH the development of advanced technologies such as onboard computers [1], robust flight control stacks [2], and Ad-hoc networks [3], it is feasible to control largescale intelligent UAV swarms to complete complex tasks. Nowadays, the UAV swarm has been applied in various scenarios such as reconnaissance [4], transportation [5], power inspection [6], disaster relief [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Ye¹

2021

Preprint

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<div>In this paper, we aim to design a deep reinforcement learning(DRL) based control solution to navigate a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under provide optimal communication coverage for the ground mobile users. Compared with existing DRL-based solutions that mainly solve the problem with global observation and centralized training, a practical and efficient Decentralized Training and Decentralized Execution(DTDE) framework is desirable to train and deploy each UAV in a distributed manner. To this end, we propose a novel DRL approach named Deep Recurrent Graph Network(DRGN) that makes use of Graph Attention Network-based Flying Ad-hoc Network(GAT-FANET) to achieve inter-UAV communications and Gated Recurrent Unit (GRU) to record historical information. We conducted extensive experiments to define an appropriate structure for GAT-FANET and examine the performance of DRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and four heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of DRGN.</div>

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Benchmark Analysis of Jetson TX2, Jetson Nano and Raspberry PI using Deep-CNN

Cited by 164 publications

References 13 publications

Analytical Review of Cybersecurity for Embedded Systems

Analytical Review of Cybersecurity for Embedded Systems

Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

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