PTM-based hybrid error-detection architecture for ARM microprocessors

Refine BING using effective cascade ranking

Motion control systems with distributed architecture where multiple input/output devices are connected to the upper layer controller by fast digital communication (fieldbus) became an industrial standard. This paper presents design of a modular input/output device which can process signals from multiple sensors, drive multiple actuators and act as a Slave or Master node in EtherCAT fieldbus network.User-defined algorithms can be easily implemented to preprocess input signals, combine multiple signals or close local control loops with extremely high sampling rates which makes the difference to standard off-the-shelf solutions. To meet these requirements and simplify hardware design, our device is based on System-on-Chip with both programmable logic (FPGA) and classic processor (CPU) ARM cores. Data processing including user algorithms can be done entirely in FPGA which provides very low latency and no jitter, and also on CPU for more complex computations with advantage of tight integration between FPGA and CPU. In this paper we provide description of hardware design, system architecture and typical applications.

A novel BRAM content accessing and processing method based on FPGA configuration bitstream

Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete tool-chain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal in the presence of single bit upsets caused by radiation induced soft errors. Each system under analysis executes different software workloads both from benchmark suites and from real applications.

Evaluating reliability through soft error triggered exceptions at ARM Cortex-A9 microprocessor

Field-Programmable Gate Arrays (FPGAs) have increased in complexity over the last few years. Now available in the form of Systems-on-Chip (SoCs) such as Xilinx Zynq or Intel Stratix, users are offered significant flexibility in deciding the best approach to execute their Deep Learning (DL) model: a) in a fixed, hardwired general-purpose processor, or b) using the programmable logic to implement application-specific processing cores. While the latter choice offers the best performance and energy efficiency, the programmable logic’s limited size requires advanced strategies for mapping large models onto hardware. In this work, we investigate using a soft-core Graphics Processing Unit (GPU), implemented in the FPGA, to execute different Convolutional Neural Networks (CNNs). We evaluate the performance, area, and energy tradeoffs of running each layer in a) an ARM Cortex-A9 with Neon extensions and in b) the soft-core GPU, and find that the GPU overlay can provide a mean acceleration of 5.9\(\times \) and 1.8\(\times \) in convolution and max-pooling layers respectively compared to the ARM core. Finally, we show the potential of the collaborative execution of CNNs using these two platforms together, with an average speedup of 2\(\times \) and 4.8\(\times \) compared to using only the ARM core or the soft GPU, respectively.KeywordsFPGASoft coreGPUSystem-on-chipConvolutional Neural Networks

Test program generation and simulation is the most widely used approach to functional verification of microprocessors. High complexity of modern hardware designs creates a demand for automated tools that are able to generate test programs covering non-trivial situations in microprocessor functioning. The majority of such tools use test program templates that describe scenarios to be covered in an abstract way. This provides verification engineers with a flexible way to describe a wide range of test generation tasks with minimum effort. Test program templates are developed in special domain-specific languages. These languages must fulfill the following requirements: (1) be simple enough to be used by verification engineers with no sufficient programming skills; (2) be applicable to various microprocessor architectures and (3) be easy to extend with facilities for describing new types of test generation tasks. The present work discusses the test program template description language used in the reconfigurable and extensible test program generation framework MicroTESK being developed at ISP RAS. It is a flexible Ruby-based domain-specific language that allows describing a wide range of test generation tasks in terms of hardware abstractions. The tool and the language have been applied in industrial projects dedicated to verification of MIPS and ARM microprocessors.

All-Programmable System-on-Chips (APSoCs) constitute a compelling option for employing applications in radiation environments thanks to their high-performance computing and power efficiency merits. Despite these advantages, APSoCs are sensitive to radiation like any other electronic device. Processors embedded in APSoCs, therefore, have to be adequately hardened against ionizing-radiation to make them a viable choice of design for harsh environments. This paper proposes a novel lockstep-based approach to harden the dual-core ARM Cortex-A9 processor in the Xilinx Zynq-7000 APSoC against radiation-induced soft errors by coupling it with a MicroBlaze TMR subsystem in the programmable logic (PL) layer of the Zynq. The proposed technique uses the concepts of checkpointing along with roll-back and roll-forward mechanisms at the software level, i.e. software redundancy, as well as processor replication and checker circuits at the hardware level (i.e. hardware redundancy). Results of fault injection experiments show that the proposed approach achieves high levels of protection against soft errors by mitigating around 98% of bit-flips injected into the register files of both ARM cores while keeping timing performance overhead as low as 25% if block and application sizes are adjusted appropriately. Furthermore, the incorporation of the roll-forward recovery operation in addition to the roll-back operation improves the Mean Workload between Failures (MWBF) of the system by up to ≈19% depending on the nature of the running application, since the application can proceed faster, in a scenario where a fault occurs, when treated with the roll-forward operation rather than roll-back operation. Thus, relatively more data can be processed before the next error occurs in the system.

Post-Mastectomy Acute Skin Toxicity Assessment Between Particle and Photon Therapy

BackgroundLymphovascular invasion (LVI) plays an important role in determining the risk of lymph node metastasis (LNM) in T1 colorectal cancer (CRC) patients and influencing treatment decisions and patient outcomes.ObjectiveThis study evaluated how the detection of LVI varies between Dutch laboratories and investigated its impact on the treatment and oncological outcomes of T1 CRC patients.MethodsPathology reports and clinical data of T1 CRC patients who underwent local resection between 2015 and 2019 were obtained from the Dutch nationwide pathology databank (Palga cohort, n = 5513). Data on the standard of LVI diagnosis (H&E/Immunohistochemistry) were not available. We categorized laboratories as low, average, or high detectors and evaluated the impact of LVI detection practice on the surgical resection rate and the proportion of LNM‐negative (LNM‐) surgeries. In the second part of the study, we used the Dutch T1 CRC Working Group cohort (n = 1268) to evaluate the impact of LVI detection practice on cancer recurrences during follow‐up. Multivariable logistic regression analyses and Cox proportional hazard regression were used to study the association between LVI detection practice and the outcomes.ResultsIn the PALGA cohort, the proportion of surgical resections after local resection of a T1 CRC was significantly higher among patients diagnosed by laboratories with a high LVI detection rate (high vs. low: adjusted OR [aOR] 1.87; 95% confidence interval [CI] 1.52–2.31) as was the proportion of LNM‐surgeries (aOR 1.73; 95% CI 1.39–2.15). In the second cohort, no significant difference was observed in cancer recurrences among patients diagnosed in laboratories with high detection rates compared with low detection rates (aHR 2.23; 95% CI 0.94–5.23).ConclusionThese findings suggest that a high detection rate of LVI does not improve oncological outcomes and may expose more patients to unnecessary oncological surgery, emphasizing the need for standardization of LVI diagnosis.

High-precision radiotherapy with proton beams is frequently used in the management of aggressive soft tissue sarcoma (STS) and is often combined with doxorubicin (Dox), the first-line chemotherapy for STS. However, current treatment approaches continue to result in high local recurrence rates often occurring within the treatment field. This strongly indicates the need of optimized treatment protocols taking the vast heterogeneity of STS into account, thereby fostering personalized treatment approaches. Here, we used preclinical STS models to investigate the radiation response following photon (X) or proton (H) irradiation alone and in combination with different treatment schedules of Dox. As preclinical models, fibrosarcoma (HT-1080), undifferentiated pleiomorphic sarcoma (GCT), and embryonal rhabdomyosarcoma (RD) cell lines were used; the latter two are mutated for TP53. The cellular response regarding clonogenic survival, apoptosis, cell-cycle distribution, proliferation, viability, morphology, and motility was investigated. The different STS cell types revealed a dose-dependent radiation response with reduced survival, proliferation, viability, and motility whereas G2/M phase arrest as well as apoptosis were induced. RD cells showed the most radiosensitive phenotype; the linear quadratic model fit could not be applied. In combined treatment schedules, Dox showed the highest efficiency when applied after or before and after radiation; Dox treatment only before radiation was less efficient. GCT cells were the most chemoresistant cell line in this study most probably due to their TP53 mutation status. Interestingly, similar additive effects could be observed for X or H irradiation in combination with Dox treatment. However, the additive effects were determined more frequently for X than for H irradiation. Thus, further investigations are needed to specify alternative drug therapies that display superior efficacy when combined with H therapy.

Hidden delay defects (HDDs) are small delay defects that pass all at-speed tests at nominal capture time. They are an important indicator of latent defects that lead to early-life failures and aging problems that are serious especially in autonomous and medical applications. An effective way to screen out HDDs is to use Faster-than-At-Speed Testing (FAST) to observe outputs of sensitized non-critical paths which are expected to be stable earlier than nominal capture time. To improve the reliability of current and future designs, it is important to learn about the population of HDDs using logic diagnosis. We present the very first logic fault diagnosis technique that is able to identify HDDs by analyzing fail logs produced by FAST. Even with aggressive FAST testing, HDDs generate only very few failing test response bits. To overcome this severe challenge, we propose new backtracing and response matching methods that yield high diagnostic success rates even with very limited amount of failure data. The performance and scalability of our HDD diagnosis method is validated using fault injection campaigns with large benchmark circuits.

This paper presents an analysis of the impact of selective software-based techniques to detect faults in microprocessor systems. A set of algorithms is implemented, compiled to a microprocessor and selected variables of the code are hardened with software-based techniques. Seven different methods that choose which variables are hardened are introduced and compared. The system is implemented over a miniMIPS microprocessor and a fault injection campaign is performed in order to verify the feasibility and effectiveness of each selective fault tolerance approach. Results can lead designers to choose more wisely which variables of the code should be hardened considering detection rates and hardening overheads.

Compliance with colonoscopy among elderly individuals participating in colorectal cancer (CRC) screening programs is unsatisfactory, despite a high detection rate of bowel-related diseases. In this study, our aim was to analyze the impact of risk factors on the trends of compliance and detection rates in colonoscopy among high-risk individuals aged 60-74. A retrospective study was conducted on the high-risk individuals aged 60-74 participating in the 2021 CRC screening program in Tianjin, China. Logistic regression analyses, including both univariate and multivariate analyses, were performed to explore the impact of different risk factors on colonoscopy compliance among the high-risk individuals. Besides, the study investigated the influence of various risk factors on the detection rates of bowel-related diseases among the high-risk individuals who underwent colonoscopy. A total of 24,064 high-risk individuals were included, and 5478 individuals received a free colonoscopy, with an overall compliance of 22.76%. Among them, the adenoma detection rate was 55.46%. Males and individuals with a positive FIT had high compliance and detection rates for CRC, advanced adenomas (AA), advanced colorectal neoplasia (ACN), and colorectal neoplasm (CN). Individuals aged 70-74 were associated with low compliance but high CRC, ACN, and CN detection rates. Individuals who reported a history of chronic constipation, bloody mucous, and CRC in first-degree relative showed high compliance but no significantwere associated with the detection rates of CRC, AA, and CN. This study reported several risk factors associated with the screening behaviors for CRC. Patterns and trends in CRC, AA, ACN, and CN compliance and detection rates correlate with risk factors.

Objective To evaluate the performance of three antibody kits for novel coronavirus (SARS-CoV-2) and to investigate the feasibility and advantages of them in clinical application. Methods A total of 104 patients who were admitted to Guangzhou Eighth People's Hospital with COVID-19 from January to February 2020 were selected as research group. Fifty-one healthy subjects were selected during the same period as negative control group. Serum antibodies (IgM/IgG) against SARS-CoV-2 were detected using two kinds of colloidal gold kits (A and B kits) and one chemiluminescence kit (C kit). The positive rates of SARS-CoV-2 nucleic acid in different samples from patients with COVID-19 were retrospectively analyzed. Results The clinical sensitivity of A kit to detect SARS-CoV-2-specific IgM and IgG was 77.88% (81/104) and 65.38% (68/104), respectively, and the clinical specificity was 70.59% (36/51) and 100.00% (51/51). However, the false positive rate in IgM detection was as high as 29.41% (15/51). The sensitivity of B kit to test total antibodies to SARS-CoV-2 was 63.46% (66/104), and the clinical specificity was 94.12% (48/51). The clinical sensitivity of C kit to detect SARS-CoV-2-specific IgM and IgG were respectively 31.73% (33/104) and 64.42% (67/104), and the clinical specificity were both 98.04% (50/51). There was a moderate correlation between the detection results of two colloidal gold kits and the chemiluminescence kit with the Kappa values of 0.462 and 0.587 (Z=6.157, P<0.01; Z=7.345, P<0.01). C kit had the highest positive detection rate for IgG, and would be more reliable to be used for IgG detection in COVID-19 patients 14 d after onset. The total positive detection rate of nucleic acid in all types of samples was 63.46% (66/104). The highest positive detection rate was in throat swabs or sputum samples, followed by those in blood samples and anal swabs. No viral nucleic acid was detected in urine samples for the time being. Conclusions SARS-CoV-2-specific antibodies could be detected in the early or late stage of COVID-19. The method of antibody detection has the advantages of shorter detection time, simple operation and high biological safety, indicating that it could be used as a supplementary or auxiliary detection for the diagnosis of suspected COVID-19 cases with negative nucleic acid test results. The chemiluminescence kit has good sensitivity and specificity, and is well recommended for clinical laboratories. Key words: SARS-CoV-2; Chemiluminescence immunoassay; Colloidal gold immunoaasay; Nucleic acid detection; Performance evaluation; Clinical application