Towards an Integrated GPU Accelerated SoC as a Flight Computer for Small Satellites

Adams, Caleb; Spain, Allen; Parker, Jackson; Hevert, Matthew; Roach, J.V.; Cotten, David L.

doi:10.1109/aero.2019.8741765

Cited by 23 publications

(9 citation statements)

References 2 publications

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“…With edge computing, the access delay can be reduced, and the radio resource can also be utilized efficiently. Benefit from the development of satellite on-board processing techniques [4], edge computing-enhanced satellite networks have also become a hot topic for integrated satellite and terrestrial networks [2,5,6]. Thus, edge computing-enhanced satellite-assisted IoT (S-IoT) receives lots of attention in both industrial and academic areas [7,8].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

Leng

et al. 2021

Wireless Communications and Mobile Computing

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Satellite-assisted internet of things (S-IoT), especially the S-IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on-board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed-integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max-min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long-term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.

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Section: Introductionmentioning

confidence: 99%

Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

Leng

et al. 2021

Wireless Communications and Mobile Computing

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“…NASA has conducted several studies from a radiation perspective on different GPUs including from both Nvidia and AMD [11,12,14]. Notable, Salazar et al have conducted radiation testing on five COTS graphic cards, of which two AMD GPUs and three Nvidia GPUs, aiming for application on the International Space Station (ISS) in low earth orbit (LEO) radiation environment [14].…”

Section: Related Workmentioning

confidence: 99%

Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space

Bruhn

Tsog

Kunkel³

et al. 2020

CEAS Space J

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The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imaging. Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links. Many missions and applications can benefit significantly from onboard cloud computing similarly to Earth-based cloud services. Hence, enabling space systems to provide near real-time data and enable low latency distribution of critical and time sensitive information to users. In addition, the downlink capability can be more effectively utilized by applying more onboard processing to reduce the data and create high value information products. This paper discusses current implementations and roadmap for leveraging high performance computing tools and methods on small satellites with radiation tolerant hardware. This includes runtime analysis with benchmarks of convolutional neural networks and matrix multiplications using industry standard tools (e.g., TensorFlow and PlaidML). In addition, a ½ CubeSat volume unit (0.5U) (10 × 10 × 5 cm3) cloud computing solution, called SpaceCloud™ iX5100 based on AMD 28 nm APU technology is presented as an example of heterogeneous computer solution. An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.

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“…On the other hand, DNNs are increasingly employed in safety-critical disciplines like healthcare [1], automotive [2], avionics [3], space [4], railway [31], and industry [6] to extract useful information from complex raw data. Hence, the dependability of deployed accelerator-based systems is rising in importance.…”

Section: Introductionmentioning

confidence: 99%

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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Nowadays, artificial intelligence (AI) and deep learning (DL) progressively adapt to various spheres of our lives. These disciplines contain safety-critical applications such as autonomous driving with a high risk of human injury in the case of malfunction, requiring a high promise of dependability. Even the dependability becomes more crucial as shrinking CMOS technology feature size worsens the resilience concerns due to factors like aging. This paper addresses the overarching dependability issue of advanced deep neural networks (DNN) accelerators from the aging perspective. Especially, a comprehensive survey and taxonomy of techniques used to evaluate and mitigate aging effects are introduced. We cover different aging effects like permanent faults, timing errors, and lifetime issues. We review research by the layer-wise approach and categorize several resilience classes to bring out major features. The concluding part of this review highlights the questions answered and several future research directions. This study is expected to benefit researchers in different areas of DNN deployment, especially the dependability of this emergent paradigm.

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Towards an Integrated GPU Accelerated SoC as a Flight Computer for Small Satellites

Cited by 23 publications

References 2 publications

Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

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