AI in space: applications examples and challenges

Furano, Gianluca; Tavoularis, Antonis; Rovatti, Marco

doi:10.1109/dft50435.2020.9250908

Cited by 23 publications

(12 citation statements)

References 8 publications

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“…In recent years, S-CPS and onboard processors in space have been striving for more performance, fueled by more advanced mission requirements and higher expectations for onboard electronics resulting from the advancement of commercial technology [17,36]. However, reliability and dependability [9,50] remain key concerns in the space domain because, with high levels of cosmic rays, errors are more frequent than at ground level and occur too often for a system to perform its intended mission reliably.…”

Section: Design Trends For Cps In Spacementioning

confidence: 99%

“…New Space missions with smaller, more cost-effective satellites tend to rely more on Commercial Off-the-Shelf (COTS) electronics to design the spacecraft [43]. These components offer significantly more performance at a lower cost, sacrificing reliability, which can be tolerated for non-critical applications, such as in CubeSats [43] or for non-critical machine learning workloads [17]. However, even for these satellite missions, certain aspects, such as communication and control, still require some radiation tolerance in COTS solutions, often guaranteed with watchdog timers.…”

Section: Design Trends For Cps In Spacementioning

confidence: 99%

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Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space

Rogenmoser¹,

Tortorella²,

Rossi³

et al. 2023

Preprint

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Space Cyber-Physical Systems (S-CPS) such as spacecraft and satellites strongly rely on the reliability of onboard computers to guarantee the success of their missions. Relying solely on radiation-hardened technologies is extremely expensive, and developing inflexible architectural and microarchitectural modifications to introduce modular redundancy within a system leads to significant area increase and performance degradation as well. To mitigate the overheads of traditional radiation hardening and modular redundancy approaches, we present a novel Hybrid Modular Redundancy (HMR) approach, a redundancy scheme that features a cluster of RISC-V processors with a flexible on-demand dual-core and triple-core lockstep grouping of computing cores with runtime split-lock capabilities. Further, we propose two recovery approaches, software-based and hardware-based, trading off performance and area overhead. Running at 430 MHz, our fault-tolerant cluster achieves up to 1160 MOPS on a matrix multiplication benchmark when configured in non-redundant mode and 617 and 414 MOPS in dual and triple mode, respectively. A software-based recovery in triple mode requires 363 clock cycles and occupies 0.612 mm 2 , representing a 1.3% area overhead over a non-redundant 12-core RISC-V cluster. As a high-performance alternative, a new hardware-based method provides rapid fault recovery in just 24 clock cycles and occupies 0.660 mm 2 , namely ∼9.4% area overhead over the baseline non-redundant RISC-V cluster. The cluster is also enhanced with split-lock capabilities to enter one of the available redundant modes with minimum performance loss, allowing execution of a safety-critical portion of code with 310 and 23 clock cycles overhead for entry and exit, respectively. The proposed system is the first to integrate these functionalities on an open-source RISC-V-based compute device, enabling finely tunable reliability vs. performance trade-offs.

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Section: Design Trends For Cps In Spacementioning

confidence: 99%

Section: Design Trends For Cps In Spacementioning

confidence: 99%

Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space

Rogenmoser¹,

Tortorella²,

Rossi³

et al. 2023

Preprint

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“…Introducing additional noise into already noisy data will not significantly degrade the result. These computationally intensive and noise-tolerant applications however could greatly benefit from higher processing power [7].…”

Section: Introductionmentioning

confidence: 99%

On-Demand Redundancy Grouping: Selectable Soft-Error Tolerance for a Multicore Cluster

Rogenmoser¹,

Wistoff²,

Vogel³

et al. 2022

Preprint

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With the shrinking of technology nodes and the use of parallel processor clusters in hostile and critical environments, such as space, run-time faults caused by radiation are a serious cross-cutting concern, also impacting architectural design. This paper introduces an architectural approach to run-time configurable soft-error tolerance at the core level, augmenting a six-core open-source RISC-V cluster with a novel On-Demand Redundancy Grouping (ODRG) scheme. ODRG allows the cluster to operate either as two fault-tolerant cores, or six individual cores for high-performance, with limited overhead to switch between these modes during run-time. The ODRG unit adds less than 11% of a core's area for a three-core group, or a total of 1% of the cluster area, and shows negligible timing increase, which compares favorably to a commercial state-ofthe-art implementation, and is 2.5× faster in fault recovery re-synchronization. Furthermore, unlike other implementations, when redundancy is not necessary, the ODRG approach allows the redundant cores to be used for independent computation, allowing up to 2.96× increase in performance for selected applications.

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“…In recent years, research in the space community has shown a growing interest in the application of Artificial Intelligence (AI), and in particular Deep Learning (DL), on board spacecrafts in view of its potential advantages [1][2][3][4][5]. One main reason is due to the high potential demonstrated by Deep Neural Network (DNN) models for many different space applications, such as object-detection [3] and recognition, image scene classification [6,7], super-resolution [8], agricultural-crop detection [9], and change detection [10], outperforming classical approaches both in terms of performance and time to design.…”

Section: Introductionmentioning

confidence: 99%

“…The deployment of DNNs on board spacecraft might also mitigate the problem of the increasing number of sensor data that must be downloaded to ground [1,5,11]. Indeed, less usable data, like cloud-covered images, can be identified, tagged, pre-filtered, discarded, or selectively compressed [2,3].…”

Section: Introductionmentioning

confidence: 99%

An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study

et al. 2021

Self Cite

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In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on board Earth Observation (EO) satellites might lead to numerous advantages in terms of mitigation of downlink bandwidth constraints, costs, and increment of the satellite autonomy. In this framework, the CloudScout project, funded by the European Space Agency (ESA), represents the first time in-orbit demonstration of a Convolutional Neural Network (CNN) applied to hyperspectral images for cloud detection. The first instance of this use case has been done with an INTEL Myriad 2 VPU on board a CubeSat optimized for low cost, size, and power efficiency. Nevertheless, this solution introduces multiple drawbacks due to its design not specifically being for the space environment, thus limiting its applicability to short-lifetime Low Earth Orbit (LEO) applications. The current work provides a benchmark between the Myriad 2 and our custom hardware accelerator designed for Field Programmable Gate Arrays (FPGAs). The metrics used for comparison include inference time, power consumption, space qualification, and components. The obtained results show that the FPGA-based solution is characterized by a reduced inference time, and a higher possibility of customization, but at the cost of greater power consumption and a longer Time to Market. As a conclusion, the proposed approach might extend the potential market of DL-based solutions to long-term LEO or interplanetary exploration missions through deployment on space-qualified FPGAs, with a limited cost in energy efficiency.

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AI in space: applications examples and challenges

Cited by 23 publications

References 8 publications

Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space

Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space

On-Demand Redundancy Grouping: Selectable Soft-Error Tolerance for a Multicore Cluster

An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study

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