TID and SEE Characterization of Microsemi's 4th Generation Radiation Tolerant RTG4 Flash-Based FPGA

Rezzak, Nadia; Wang, Jih-Jong; Dsilva, Durwyn; Jat, Narayan

doi:10.1109/redw.2015.7336739

Cited by 29 publications

(10 citation statements)

References 4 publications

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“…13,37 The RTG4 FPGA manufactured by MicroSemi is a radiation tolerant FPGA released in 2015. The device has been tested up to 100s of krad(Si), 38 and is now used in standard spacecraft computing components. 39 In summaries collected in 2017 14 the RTG4 had the highest performance per watt for matrix multiply tasks of all processors considered.…”

Section: Current and Near-future Space Processorsmentioning

confidence: 99%

Evaluating embedded hardware for high-order wavefront sensing and control

Belsten,

Milani,

Pogorelyuk

et al. 2023

Techniques and Instrumentation for Detection of Exoplanets XI

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Future space telescopes such as the Habitable Worlds Observatory (HWO) will use coronagraphs and wavefront control to achieve the approximate 1010 starlight suppression necessary to directly image Earth-like exoplanets. Wavefront control algorithms such as Electric Field Conjugation (EFC) will control thousands of actuators at cadences of seconds or minutes. EFC uses a Jacobian matrix which maps Deformable Mirror (DM) voltages to the change in electric field at the image plane. The Jacobian matrix grows in size with the number of pixels, DM actuators, and spectral channels. EFC on proposed future telescopes like HabEx and LUVOIR will require as much as 25 GFLOPS (floating point operations per second). This level of compute density has never been achieved on radiation-hardened processors that are used on NASA Class-A missions such as the Roman Space Telescope. Previous work has focused on estimating the Compute Density (CD) of processors using assumptions about memory access characteristics and the parallelizability of algorithm implementation. Such analysis produces large uncertainty due to the assumptions necessary to compute CD. To refine the estimates of EFC compute capability of current generation processors, we determine the FLOPS performance of processors using benchmark tests which represent the operations mix and memory access patterns of EFC. The expected EFC iteration computation period on future space telescopes based on application benchmarks is reported. We have created a ray tracing optical model for the telescope assembly as well as a physical optics model for the telescope and coronagraph for the purposes of testing HOWFC algorithms. This testing can be applied to CPUs and FPGAs, representing a range of potential compute architectures.

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Section: Current and Near-future Space Processorsmentioning

confidence: 99%

Evaluating embedded hardware for high-order wavefront sensing and control

Belsten,

Milani,

Pogorelyuk

et al. 2023

Techniques and Instrumentation for Detection of Exoplanets XI

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“…• RTG4 FPGA from Microsemi, which can tolerate a TID up to 160 Krad [44]; • GR740 from CAES, which features the LEON4 microprocessor. This board can tolerate a TID of 300 Krad [45].…”

Section: Radiation Hardened Devicesmentioning

confidence: 99%

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

Diana

Fanucci

2021

Remote Sensing

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Oil spills represent one of the major threats to marine ecosystems. Satellite synthetic-aperture radar (SAR) sensors have been widely used to identify oil spills due to their ability to provide high resolution images during day and night under all weather conditions. In recent years, the use of artificial intelligence (AI) systems, especially convolutional neural networks (CNNs), have led to many important improvements in performing this task. However, most of the previous solutions to this problem have focused on obtaining the best performance under the assumption that there are no constraints on the amount of hardware resources being used. For this reason, the amounts of hardware resources such as memory and power consumption required by previous solutions make them unsuitable for remote embedded systems such as nano and micro-satellites, which usually have very limited hardware capability and very strict limits on power consumption. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. It also provides many additional very significant advantages, such as having shorter inference times, requiring shorter training times, and avoiding transmission of irrelevant data. Our goal is to allow embedded low power remote devices such as satellite systems for remote sensing to be able to directly run CNNs on board, so that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR images.

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“…[92] reports the results of heavy ions and proton tests for SEE of an IGLOO2. "High current" status triggered by particles (suspected SEL) is reported (while, for instance, spacegrade RTG4 is SEL immune [93]). c) Space-grade SRAM-based (e.g., Xilinx Virtex 5QV): These devices are typically well characterized in terms of effects of radiation and provide meaningful improvements over COTS SRAM based (Ref.…”

Section: Hardware Modelsmentioning

confidence: 99%

“…This means that the same level of fault tolerance employed for ASICs can be considered enough. In [93] the tolerance to TID effect of the RTG4 is compared with the one of the SmartFusion2 and a significant improvement is found, with a TID tolerance tested up to 160 krad. No SELs were observed for an LET of 103 MeV ⋅ cm 2 ∕mg at 100°C, and the SER of the sequential elements (flip-flops with TMR) is at least three orders of magnitude better than the simple flip-flops in the SmartFusion2.…”

Section: Hardware Modelsmentioning

confidence: 99%

Leveraging the Openness and Modularity of RISC-V in Space

Mascio¹,

Menicucci²,

Gill³

et al. 2019

Journal of Aerospace Information Systems

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This paper proposes a roadmap to address present and future needs in space systems with RISC-V processors. RISC-V is an open and modular instruction set architecture, which is rapidly growing in popularity in terrestrial applications. To satisfy different applications with contrasting requirements in satellite data systems, four different types of processors are identified: 1) low-area/low-power microcontrollers, 2) on-board computers, 3) general-purpose processors for payloads, and 4) enhanced payload processors for artificial intelligence. Several solutions based on RISC-V are proposed for each of these types of processors and compared with proprietary commercial-off-the-shelf and spacegrade solutions. An extensive analysis of the results available from literature is conducted to show that RISC-V has the potential to solve such a wide range of needs. This paper will also show the unprecedented number of open-source implementations and models that were developed in a relative short time on a single instruction set architecture. Future space systems could benefit from many of those developments, and this work identifies and highlights what is still missing to satisfy the specific needs of processors for space, especially in terms of fault tolerance and technology readiness level.

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TID and SEE Characterization of Microsemi's 4th Generation Radiation Tolerant RTG4 Flash-Based FPGA

Cited by 29 publications

References 4 publications

Evaluating embedded hardware for high-order wavefront sensing and control

Evaluating embedded hardware for high-order wavefront sensing and control

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

Leveraging the Openness and Modularity of RISC-V in Space

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