Microprocessor fault-tolerance via on-the-fly partial reconfiguration

This paper presents a novel approach to exploit FPGA dynamic partial reconfiguration to improve the fault tolerance of complex microprocessor-based systems, with no need to statically reserve area to host redundant components. The proposed method not only improves the survivability of the system by allowing the online replacement of defective key parts of the processor, but also provides performance graceful degradation by executing in software the tasks that were executed in hardware before a fault and the subsequent reconfiguration happened. The advantage of the proposed approach is that thanks to a hardware hypervisor, the CPU is totally unaware of the reconfiguration happening in real-time, and there's no dependency on the CPU to perform it. As proof of concept a design using this idea has been developed, using the LEON3 open-source processor, synthesized on a Virtex 4 FPG

An investigation how quantitative easing programme, Vickers’ ring-fencing regulation and the ‘Brexit’ announcement impact on UK banking sector

In this paper, ‘Events study analysis’ is used to analyse the impact of Vickers’ Ring-fencing regulation, Quantitative Easing programme and the United Kingdom’s vote to leave the European Union (‘Brexit’) on the UK banking system. Ten banks have been included in the study and the stock price data for each of them was collected from the 14th January 2011 to the 30th of July 2016. We find that banks affected by Vickers’ regulation did have negative abnormal returns as the policy progressed, indicating that the policy may not be the best way to limit risk in banks. The results also show that Quantitative Easing does affect the banks’ abnormal returns positively and that ‘bigger’ banks benefit more from its implementation. Finally, we discover that the ‘Brexit’ vote did cause negative abnormal returns across all banks, however, it was the smaller ‘unaffected’ banks which suffered the most

Energy versus Output Quality of Non-volatile Writes in Intermittent Computing

We explore how to improve the energy performance of battery-less Internet of Things (IoT) devices at the cost of a reduction in the quality of the output. Battery-less IoT devices are extremely resource-constrained energy-harvesting devices. Due to erratic energy patterns from the ambient, their executions become intermittent; periods of active computation are interleaved by periods of recharging small energy buffers. To cross periods of energy unavailability, a device persists application and system state onto Non-Volatile Memory (NVM) in anticipation of energy failures. We purposely control the energy invested in these operations, representing a major energy overhead, when using Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) as NVM. As a result, we abate the corresponding overhead, yet introduce write errors. Based on 1.9+ trillion experimental data points, we illustrate whether this is a gamble worth taking, when, and where. We measure the energy consumption and quality of output obtained from the execution of nine diverse benchmarks on top of seven different platforms. Our results allow us to draw three key observations: i) the trade-off between energy saving and reduction of output quality is program-specific; ii) the same trade-off is a function of a platform's specific compute efficiency and power figures; and iii) data encoding and input size impact a program's resilience to errors. As a paradigmatic example, we reveal cases where we achieve up to 50% reduction in energy consumption with negligible effects on output quality, as opposed to settings where a minimal energy gain causes drastic drops in output quality

Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms

DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, jointly considering node-level heterogeneity of edge devices, and application-specific accuracy and performance requirements. Our proposed approach combinatorially optimizes heterogeneity-aware workload partitioning and dynamic accuracy configuration of DNN models to ensure performance and accuracy guarantees. We tested our approach on an edge cluster of Odroid XU4, Raspberry Pi4, and Jetson Nano boards and achieved an average gain of 41.52% in performance and 5.2% in output accuracy as compared to state-of-the-art workload distribution strategies

An Integrative Genomics Approach to Biomarker Discovery in Breast Cancer

Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the combined contribution of multiple genetic variants acting within genes and putative biological pathways, and to identify novel genes and biological pathways that could not be identified using traditional GWAS. The results show that genes containing SNPs associated with risk for breast cancer are functionally related and interact with each other in biological pathways relevant to breast cancer. Additionally, we identified novel genes that are co-expressed and interact with genes containing SNPs associated with breast cancer. Integrative analysis combining GWAS information with gene expression data provides functional bridges between GWAS findings and biological pathways involved in breast cancer

Run-time Resource Management in CMPs Handling Multiple Aging Mechanisms

Abstract—Run-time resource management is fundamental for efficient execution of workloads on Chip Multiprocessors. Application- and system-level requirements (e.g. on performance vs. power vs. lifetime reliability) are generally conflicting each other, and any decision on resource assignment, such as core allocation or frequency tuning, may positively affect some of them while penalizing some others. Resource assignment decisions can be perceived in few instants of time on performance and power consumption, but not on lifetime reliability. In fact, this latter changes very slowly based on the accumulation of effects of various decisions over a long time horizon. Moreover, aging mechanisms are various and have different causes; most of them, such as Electromigration (EM), are subject to temperature levels, while Thermal Cycling (TC) is caused mainly by temperature variations (both amplitude and frequency). Mitigating only EM may negatively affect TC and vice versa. We propose a resource orchestration strategy to balance the performance and power consumption constraints in the short-term and EM and TC aging in the long-term. Experimental results show that the proposed approach improves the average Mean Time To Failure at least by 17% and 20% w.r.t. EM and TC, respectively, while providing same performance level of the nominal counterpart and guaranteeing the power budget

Could nearby star-forming galaxies light up the point-like neutrino sky?

Star-forming and starburst galaxies, which are well-known cosmic-rays reservoirs, are expected to emit gamma-rays and neutrinos predominantly via hadronic collisions. In this Letter, we analyze the 10-year Fermi-LAT spectral energy distributions of 13 nearby galaxies by means of a physical model which accounts for high-energy proton transport in starburst nuclei and includes the contribution of primary and secondary electrons. In particular, we test the hypothesis that the observed gamma-ray fluxes are mostly due to star-forming activity, in agreement with the available star formation rates coming from IR and UV observations. Through this observation-based approach, we determine the most-likely neutrino counterpart from star-forming and starburst galaxies and quantitatively assess the ability of current and upcoming neutrino telescopes to detect them as point-like sources. Remarkably, we find that the cores of the Small Magellanic Cloud and the Circinus galaxy are potentially observable by KM3NeT/ARCA with 6 years of observation. Moreover, most of the nearby galaxies are likely to be just a factor of a few below the KM3NeT and IceCube-Gen2 point-like sensitivities. After investigating the prospects for detection of gamma-rays above TeV energies from these sources, we conclude that the joint observations of high-energy neutrinos and gamma-rays with upcoming telescopes will be an objective test for our emission model and may provide compelling evidence of star-forming activity as a tracer of neutrino production.Comment: 7 pages, 2 figure

Enhancer of zeste homolog 2 (EZH2) in pediatric soft tissue sarcomas: first implications.

Soft tissue sarcomas of childhood are a group of heterogeneous tumors thought to be derived from mesenchymal stem cells. Surgical resection is effective only in about 50% of cases and resistance to conventional chemotherapy is often responsible for treatment failure. Therefore, investigations on novel therapeutic targets are of fundamental importance. Deregulation of epigenetic mechanisms underlying chromatin modifications during stem cell differentiation has been suggested to contribute to soft tissue sarcoma pathogenesis. One of the main elements in this scenario is enhancer of zeste homolog 2 (EZH2), a methyltransferase belonging to the Polycomb group proteins. EZH2 catalyzes histone H3 methylation on gene promoters, thus repressing genes that induce stem cell differentiation to maintain an embryonic stem cell signature. EZH2 deregulated expression/function in soft tissue sarcomas has been recently reported. In this review, an overview of the recently reported functions of EZH2 in soft tissue sarcomas is given and the hypothesis that its expression might be involved in soft tissue sarcomagenesis is discussed. Finally, the therapeutic potential of epigenetic therapies modulating EZH2-mediated gene repression is considered

Enhancer of zeste homolog 2 (EZH2) in pediatric soft tissue sarcomas: first implications

Soft tissue sarcomas of childhood are a group of heterogeneous tumors thought to be derived from mesenchymal stem cells. Surgical resection is effective only in about 50% of cases and resistance to conventional chemotherapy is often responsible for treatment failure. Therefore, investigations on novel therapeutic targets are of fundamental importance. Deregulation of epigenetic mechanisms underlying chromatin modifications during stem cell differentiation has been suggested to contribute to soft tissue sarcoma pathogenesis. One of the main elements in this scenario is enhancer of zeste homolog 2 (EZH2), a methyltransferase belonging to the Polycomb group proteins. EZH2 catalyzes histone H3 methylation on gene promoters, thus repressing genes that induce stem cell differentiation to maintain an embryonic stem cell signature. EZH2 deregulated expression/function in soft tissue sarcomas has been recently reported. In this review, an overview of the recently reported functions of EZH2 in soft tissue sarcomas is given and the hypothesis that its expression might be involved in soft tissue sarcomagenesis is discussed. Finally, the therapeutic potential of epigenetic therapies modulating EZH2-mediated gene repression is considered