Design of SRAM-Based Low-Cost SEU Monitor for Self-Adaptive Multiprocessing Systems

Chen, Junchao; Andjelković, Marko; Simevski, Aleksandar; Li, Yuanqing; Skoncej, Patryk; Krstić, Miloš

doi:10.1109/dsd.2019.00080

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…However, for the self-adaptive systems for space applications it is important that the particle monitor is embedded into the target system in order to detect the radiation conditions to which the target system is exposed during the mission. An embedded low-cost SRAM-based SEU monitor is intended to be used in the online phase for the real-time SEU measurement [27]. It is essential to mention that the existing SRAM resources are utilized for SEU measurement, which minimized the area and power overhead.…”

Section: System Overviewmentioning

confidence: 99%

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

Chen

Lange

Andjelković

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in spaceborne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during runtime, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.

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Section: System Overviewmentioning

confidence: 99%

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

Chen

Lange

Andjelković

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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“…As alternative to the conventional stand-alone SRAM-based particle detectors described in previous Section, we have proposed the use of embedded SRAM as a particle detector [35]. The idea is to employ the standard on-chip SRAM memory as a particle detector in parallel to its normal data storage function.…”

Section: Embedded Sram As a Particle Detectormentioning

confidence: 99%

“…Any error that cannot be corrected by EDAC is considered as a permanent error. A detailed description of the algorithm for detection of single, double and permanent faults can be found in [35]. The number of detected faults is stored in the Register File to avoid duplicate counting of the double and permanent faults.…”

Section: Embedded Sram As a Particle Detectormentioning

confidence: 99%

“…Hence, there is a strong need for alternative solutions which can provide low-cost but accurate on-chip particle detection. Motivated by these goals, we have proposed two particle detection solutions: (i) a particle detector based on embedded SRAM [35] and (ii) a particle detector based on custom-sized pulse stretching inverter chains [36,37]. The preliminary evaluation has confirmed that both proposed solutions offer promising advantages over the state-ofthe-art particle detectors in terms of the requirements for the self-adaptive fault tolerance systems.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Particle Detectors for Space-Borne Self-Adaptive Fault-Tolerant Systems

Andjelković

Chen

Simevski

et al. 2020

2020 IEEE East-West Design &Amp; Test Symposium (EWDTS)

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The soft error rate (SER) of integrated circuits (ICs) operating in space environment may vary by several orders of magnitude due to the variable intensity of radiation exposure. To ensure the radiation hardness without compromising the system performance, it is necessary to implement the dynamic hardening mechanisms which can be activated under the critical radiation exposure. Such operating scenario requires the real-time detection of energetic particles responsible for the soft errors. Although numerous particle detection solutions have been reported, very few works address the on-chip particle detectors suited for the selfadaptive fault tolerant microprocessor systems for space missions. This work reviews the state-of-the-art particle detectors, with emphasis on two solutions for the self-adaptive systems: particle detector based on embedded SRAM and particle detector based on pulse stretching inverters.

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“…When a faulty row is read, a long codeword (including check bits) is read from the PCM and the DRAM, and data are decoded from it. In Reference [53], Chen et al propose an SEU monitor (basically, an error counter) to be integrated into an SRAM with EDAC (error detection and correction) and scrubbing.…”

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confidence: 99%

Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM

et al. 2020

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Due to transistor shrinking, intermittent faults are a major concern in current digital systems. This work presents an adaptive fault tolerance mechanism based on error correction codes (ECC), able to modify its behavior when the error conditions change without increasing the redundancy. As a case example, we have designed a mechanism that can detect intermittent faults and swap from an initial generic ECC to a specific ECC capable of tolerating one intermittent fault. We have inserted the mechanism in the memory system of a 32-bit RISC processor and validated it by using VHDL simulation-based fault injection. We have used two (39, 32) codes: a single error correction–double error detection (SEC–DED) and a code developed by our research group, called EPB3932, capable of correcting single errors and double and triple adjacent errors that include a bit previously tagged as error-prone. The results of injecting transient, intermittent, and combinations of intermittent and transient faults show that the proposed mechanism works properly. As an example, the percentage of failures and latent errors is 0% when injecting a triple adjacent fault after an intermittent stuck-at fault. We have synthesized the adaptive fault tolerance mechanism proposed in two types of FPGAs: non-reconfigurable and partially reconfigurable. In both cases, the overhead introduced is affordable in terms of hardware, time and power consumption.

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Design of SRAM-Based Low-Cost SEU Monitor for Self-Adaptive Multiprocessing Systems

Cited by 17 publications

References 22 publications

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

Solar Particle Event and Single Event Upset Prediction from SRAM-based Monitor and Supervised Machine Learning

A Review of Particle Detectors for Space-Borne Self-Adaptive Fault-Tolerant Systems

Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM

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