Benchmarking Deep Learning Inference of Remote Sensing Imagery on the Qualcomm Snapdragon And Intel Movidius Myriad X Processors Onboard the International Space Station

Dunkel, Emily; Swope, Jason; Towfic, Zaid J.; Chien, Steve; Russell, Damon; Sauvageau, Joseph; Sheldon, Douglas; Romero-Cañas, Juan; Espinosa-Aranda, José Luís; Buckley, Léonie; Hervas-Martin, Elena; Fernandez, Mark; Knox, Carrie

doi:10.1109/igarss46834.2022.9884906

Cited by 25 publications

(6 citation statements)

References 8 publications

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“…Similar to the Google Edge TPU and Nvidia Jetson devices, the Myriad VPU family, encompassing both the Myriad X and its predecessor, the Myriad 2, has found a niche in the space domain. Recognized as a reliable accelerator for NN inference, these devices have been integral to various space missions, including onboard satellites [179] and the International Space Station (ISS) [180,181]. Their adaptability and performance have made them particularly well suited for low-Earth-orbit missions [173,182,183].…”

Section: Intel Movidius Myriad X Vpumentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

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Section: Intel Movidius Myriad X Vpumentioning

confidence: 99%

Overview of AI-Models and Tools in Embedded IIoT Applications

Dini,

Diana,

Elhanashi

et al. 2024

Electronics

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“…The Intel Movidius Myriad 2 and STM32 Microcontroller, both low-power processors, have successfully executed star identification tasks using neural networks with a power usage of 0.89-1.08 W for Myriad and 1.15-1.2 W for STM32 [30]. On the International Space Station (ISS), deep learning models were evaluated on the Qualcomm Snapdragon 855 and Intel Movidius Myriad X processors, further underlining the feasibility of running a neural network onboard of satellites [31]. A variety of models were tested that were trained on images from Earth and Mars.…”

Section: B Deploying Neural Network Onboard Small Satellitesmentioning

confidence: 99%

Joint Source-and-Channel Coding for Small Satellite Applications

Kondrateva,

Dietzel,

Scheuermann

2023

2023 IEEE 48th Conference on Local Computer Networks (LCN)

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Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multispectral imagery. These satellites typically operate in low earth orbit, which poses significant challenges for data transmission due to short contact times with ground stations, low bandwidth, and high packet loss probabilities. In this paper, we introduce JSCC-SAT, which applies joint source-and-channel coding using neural networks to provide efficient and robust transmission of compressed image data for satellite applications. We evaluate our mechanism against traditional transmission schemes with separate source and channel coding and demonstrate that it outperforms the existing approaches when applied to Earth observation data of the Sentinel-2 mission.

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“…Many enterprises have also developed COTS devices (e.g., Intel Movidius Myriad X Vision Processing Unit (VPU) [16], [17], NVIDIA Jetson Nano [18], Google Coral Tensor Processing Unit (TPU) [16], [19], Intel Loihi [20], [21]), which are ready-made hardware products with dedicated Software Development Kits (SDKs), often used as hardware accelerators for embedded applications. They can mitigate the high power consumption of GPUs, while also maintaining good performance and working at high operating frequencies.…”

Section: B Cots Devicesmentioning

confidence: 99%

Exploring Key Aspects of Soft GPGPU Computing for On-board Acceleration of Artificial Intelligence Algorithms in Space Applications

Monopoli,

Zulberti,

Nannipieri

et al. 2023

2023 European Data Handling &Amp; Data Processing Conference (EDHPC)

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Intelligence has gained widespread adoption across different industrial sectors, serving as a versatile tool to carry out a diverse array of tasks, ranging from image classification and traffic forecasting to natural language processing and speech recognition. In the space domain, however, a special focus must be placed on area overhead, power consumption, and fault-tolerant solutions. In this particular scenario, soft General-Purpose Computing on Graphic Processing Units has the potential to revolutionise space-related activities. Indeed, by leveraging both Field Programmable Gate Array technology and Graphic Processing Unit computing, it becomes feasible to achieve highperformance capabilities without compromising neither power consumption nor radiation tolerance features. Moreover, the use of reconfigurable hardware can facilitate the acceleration of a wide range of Machine Learning algorithms, avoiding the drawbacks of excessive specialisation. This paper explores the State-of-the-Art in terms of hardware platforms for on-the-edge acceleration of Artificial Intelligence algorithms and compares it with a possible System-on-Chip implementation based on a soft-Graphic Processing Unit. Then, the attention is shifted towards the investigation of key aspects for future space missions, such as reliability and Dynamic Partial Reconfiguration. We point out the lack of European technological solutions, emphasising the promising potential offered by NanoXplore devices. We also discuss the importance of fault detection and mitigation techniques in space applications, covering the most commonly employed hardware methods for reliability enhancement and highlighting the lack of work in the field of General-Purpose Computing for Graphic Processing Units, especially in the space sector. Furthermore, we briefly examine the implementation of Dynamic Partial Reconfiguration over a System-on-Chip featuring a soft-Graphic Processing Unit IP. Finally, in the last section of the paper, we hint at future development of the project and conclude the work.

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Benchmarking Deep Learning Inference of Remote Sensing Imagery on the Qualcomm Snapdragon And Intel Movidius Myriad X Processors Onboard the International Space Station

Cited by 25 publications

References 8 publications

Overview of AI-Models and Tools in Embedded IIoT Applications

Overview of AI-Models and Tools in Embedded IIoT Applications

Joint Source-and-Channel Coding for Small Satellite Applications

Exploring Key Aspects of Soft GPGPU Computing for On-board Acceleration of Artificial Intelligence Algorithms in Space Applications

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