设计和分析用于卫星处理系统的自适应抗辐射 RRAM 子系统

IF 0.9 4区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Design Automation for Embedded Systems Pub Date : 2024-04-10 DOI:10.1007/s10617-024-09285-z
Daniel Reiser, Junchao Chen, Johannes Knödtel, Andrea Baroni, Miloš Krstić, Marc Reichenbach
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引用次数: 0

摘要

与传统存储器技术相比,新型 RRAM 器件具有众多优点,其中之一是其固有的抗辐射能力。因此,它们似乎适合用作卫星计算机架构中的存储器子系统。除了能抵御辐射的存储器件外,动态应用保护措施的概念也保证了系统在辐射事件中不易出错,同时还能确保在无辐射事件时的高效性能。本文介绍了首个基于 RRAM 的卫星内存子系统,该子系统可对辐射事件做出动态响应。我们将该子系统集成到一个计算平台中,该平台的处理系统采用了相同的动态原理,并实施了及时检测甚至预测辐射事件的模块。为了确定哪种保护机制是最佳的,我们研究了各种方法,并模拟了内存出错的概率。此外,我们还利用故障注入仿真研究了不同的软件算法及其辐射鲁棒性要求,从而研究其对整个系统的影响。最后,我们提出了基于动态 RRAM 的存储器子系统的潜在实施方案,其中包括不同级别的保护,可用于卫星的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Design and analysis of an adaptive radiation resilient RRAM subsystem for processing systems in satellites

Among the numerous benefits that novel RRAM devices offer over conventional memory technologies is an inherent resilience to the effects of radiation. Hence, they appear suitable for use as a memory subsystem in a computer architecture for satellites. In addition to memory devices resistant to radiation, the concept of applying protective measures dynamically promises a system with low susceptibility to errors during radiation events, while also ensuring efficient performance in the absence of radiation events. This paper presents the first RRAM-based memory subsystem for satellites with a dynamic response to radiation events. We integrate this subsystem into a computing platform that employs the same dynamic principles for its processing system and implements modules for timely detection and even prediction of radiation events. To determine which protection mechanism is optimal, we examine various approaches and simulate the probability of errors in memory. Additionally, we are studying the impact on the overall system by investigating different software algorithms and their radiation robustness requirements using a fault injection simulation. Finally, we propose a potential implementation of the dynamic RRAM-based memory subsystem that includes different levels of protection and can be used for real applications in satellites.

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来源期刊
Design Automation for Embedded Systems
Design Automation for Embedded Systems 工程技术-计算机:软件工程
CiteScore
2.60
自引率
0.00%
发文量
10
审稿时长
>12 weeks
期刊介绍: Embedded (electronic) systems have become the electronic engines of modern consumer and industrial devices, from automobiles to satellites, from washing machines to high-definition TVs, and from cellular phones to complete base stations. These embedded systems encompass a variety of hardware and software components which implement a wide range of functions including digital, analog and RF parts. Although embedded systems have been designed for decades, the systematic design of such systems with well defined methodologies, automation tools and technologies has gained attention primarily in the last decade. Advances in silicon technology and increasingly demanding applications have significantly expanded the scope and complexity of embedded systems. These systems are only now becoming possible due to advances in methodologies, tools, architectures and design techniques. Design Automation for Embedded Systems is a multidisciplinary journal which addresses the systematic design of embedded systems, focusing primarily on tools, methodologies and architectures for embedded systems, including HW/SW co-design, simulation and modeling approaches, synthesis techniques, architectures and design exploration, among others. Design Automation for Embedded Systems offers a forum for scientist and engineers to report on their latest works on algorithms, tools, architectures, case studies and real design examples related to embedded systems hardware and software. Design Automation for Embedded Systems is an innovative journal which distinguishes itself by welcoming high-quality papers on the methodology, tools, architectures and design of electronic embedded systems, leading to a true multidisciplinary system design journal.
期刊最新文献
Model predictive-based DNN control model for automated steering deployed on FPGA using an automatic IP generator tool Design and analysis of an adaptive radiation resilient RRAM subsystem for processing systems in satellites Improving edge AI for industrial IoT applications with distributed learning using consensus Profiling with trust: system monitoring from trusted execution environments Novel adaptive quantization methodology for 8-bit floating-point DNN training
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