Hibernus: sustaining computation during intermittent supply for energy-harvesting systems

A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76-100% and 49-79% respectively

Sessile macrobenthos (Ochrophyta) drives seasonal change of meiofaunal community structure on temperate rocky reefs

none7noUnlike the soft bottom meiofauna, meiofauna associated to hard substrata is poorly studied, despite its ecological relevance. Since communities of hard substrata are usually characterized by species with different life cycles and strategies from those of soft bottom assemblages, information on hard substrata meiofauna is still needed. In this study, sessile macrobenthos and the associated meiofaunal assemblages of two sites of Portofino (NW Mediterranean) were investigated in two seasons at three different depths on both sub-vertical and inclined reefs. The study aimed to assess the abundance, diversity and composition of the meiofauna and the factors structuring its assemblages. Moreover, as meiofauna is known to be dependent upon the substrate characteristics, the study investigated whether the meiofaunal patterns could be related to the sessile macrobenthos structure and composition, and to which extent. Macroalgae dominated the sessile macrobenthic assemblages, while Nematoda and Copepoda were the main meiofaunal groups. Meiofaunal higher-taxa richness and diversity resulted very high, due to the large number of different microhabitats offered by macroalgae. Macrobenthic assemblages were dominated by Rodophyta and Ochrophyta in summer, the latter dramatically collapsing in winter. The meiofaunal abundance and composition changed significantly with the season, consistently with the sessile macrobenthic assemblages, and resulted strongly correlated with Ochrophyta. Shaping the meiofaunal assemblages, macroalgae appeared to act as ecosystem engineer for the meiofauna.openLosi, V; Sbrocca, C; Gatti, G; Semprucci, F; Rocchi, M; Bianchi, C N; Balsamo, MLosi, V; Sbrocca, C; Gatti, G; Semprucci, F; Rocchi, M; Bianchi, C N; Balsamo,

Graceful performance modulation for power-neutral transient computing systems

Transient computing systems do not have energy storage, and operate directly from energy harvesting. These systems are often faced with the inherent challenge of low-current or transient power supply. In this paper, we propose “power-neutral” operation, a new paradigm for such systems, whereby the instantaneous power consumption of the system must match the instantaneous harvested power. Power neutrality is achieved using a control algorithm for dynamic frequency scaling (DFS), modulating system performance gracefully in response to the incoming power. Detailed system model is used to determine design parameters for selecting the system voltage thresholds where the operating frequency will be raised or lowered, or the system will be hibernated. The proposed control algorithm for power-neutral operation is experimentally validated using a microcontroller incorporating voltage threshold-based interrupts for frequency scaling. The microcontroller is powered directly from real energy harvesters; results demonstrate that a power-neutral system sustains operation for 4–88% longer with up to 21% speedup in application execution

Thermally-aware composite run-time CPU power models

Accurate and stable CPU power modelling is fundamental in modern system-on-chips (SoCs) for two main reasons: 1) they enable significant online energy savings by providing a run-time manager with reliable power consumption data for controlling CPU energy-saving techniques; 2) they can be used as accurate and trusted reference models for system design and exploration. We begin by showing the limitations in typical performance monitoring counter (PMC) based power modelling approaches and illustrate how an improved model formulation results in a more stable model that efficiently captures relationships between the input variables and the power consumption. Using this as a solid foundation, we present a methodology for adding thermal-awareness and analytically decomposing the power into its constituting parts. We develop and validate our methodology using data recorded from a quad-core ARM Cortex-A15 mobile CPU and we achieve an average prediction error of 3.7% across 39 diverse workloads, 8 Dynamic Voltage-Frequency Scaling (DVFS) levels and with a CPU temperature ranging from 31 degrees C to 91 degrees C. Moreover, we measure the effect of switching cores offline and decompose the existing power model to estimate the static power of each CPU and L2 cache, the dynamic power due to constant background (BG) switching, and the dynamic power caused by the activity of each CPU individually. Finally, we provide our model equations and software tools for implementing in a run-time manager or for using with an architectural simulator, such as gem5

Hibernus++: a self-calibrating and adaptive system for transiently-powered embedded devices

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This work proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over stateof- the-art approaches

Metrology For Advanced Manufacturing – The Networking Project AdvManuNet

Advanced Manufacturing is a branch of manufacturing that is considered an important driver for future economic and societal progress. The European Commission (EC) has identified Advanced Manufacturing as one of six Key Enabling Technologies (KETs) with applications across multiple industrial sectors. The networking project JNP19Net01 AdvManuNet funded by EURAMET for four years starting in June 2020 aims to accelerate the process of establishing a European Metrology Network (EMN) to strengthen Europe’s position in Advanced Manufacturing. The consortium to deliver this project comprises National Metrological Institutes (PTB, NPL, INRIM, RISE, CMI, METAS, TUBITAK, GUM), Designated Institutes (BAM), University partners (Politecnico di Torino) and the European Society for Precision Engineering and Nanotechnology (euspen) from across Europe

Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses

The role of social networks in students’ learning experiences

The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined