1,721,168 research outputs found
Novel modeling approaches for analyzing the design of distributed multi-energy systems
Full text linkQuesta tesi descrive una serie di miglioramenti metodologici nell'analisi dei sistemi energetici distribuiti e nella loro progettazione, con particolare attenzione ai sistemi che coinvolgono più di un vettore energetico (sistemi multi-energia). La questione della progettazione di tali sistemi sta diventando estremamente importante e necessita di nuovi approcci a causa di uno scenario di fruizione dell'energia che andrà a mutare notevolmente a causa di diversi cambiamenti riconducibili principalmente alla necessità di rendere la fruizione dell'energia più sostenibile e a minor impatto ambientale.This dissertation describes a set of methodological improvements in the analysis of distributed energy systems and their design, with a particular focus on systems involving more than one energy vector (multi-energy systems). The question of the planning of such systems is becoming extremely important and needing of novel approaches due to an energy fruition scenario which is going to greatly mutate due to several changes which can be mostly reconducted to the need of making the fruition of energy more sustainable and with a lesser environmental impact. Such paradigm changes are of diverse nature: coming from both technological trends, social changing paradigms and an always more impellent policy commitment towards reducing the carbon footprint of our society. As an example they can be attributed to the continuously increasing penetration of renewable non-controllable energy conversion sources, the delocalization of energy systems into a distributed paradigm, and the drastic changes of the modality of the fruition of some energy related commodities with the increasing presence of air conditioning and electric vehicles for example. Given the complex nature of such problems, and therefore the computationally demanding nature of the models needed to gain insights about their workings, a renovated focus is put on obtaining models which allow for the analyses to be undertaken in reasonable amounts of time in this novel energy systems context without compromising on the necessary level of detail into the modeling. In this thesis, we define two potential approaches in doing so, tackling two different challenges that emerged from the available literature.
We define a novel approach for the consideration of the temporal dimension in the planning of distributed energy systems. Specifically, the approach aims at properly considering the multi-decade timespan over which a decision concerning the potential adoption of energy systems has to be made. In this setting where some relevant parameters such as the capital costs for the investments in the technologies might change even significantly over such long periods. The approach is implemented within a framework having at its core an optimization problem, solved through mixed-integer programming and heuristic techniques. The validity is then tested on a realistic test case modeled by referring to an open dataset of energy related consumptions for a set of households located in the United States, where the goal of the methodology is finding the optimal year of adoption of an electricity
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storage system (if any) considering its dropping costs over the multi-year planning horizon.
We define a methodology to consider various sources of uncertainty within simulations performed by means of an already well established model: being the EnergyPLAN model for smart multi-energy systems. This is achieved by defining a framework that models a set of uncertain inputs such as the availability of solar radiation, the uncertainty in the user demands for space heating and sanitary hot water, and finally the uncertain nature of the demands of an electric vehicle fleet. The test case over which the methodology is tested is the city of Osimo, situated in Italy, which can be assumed to be a small scale multi-energy system, and about which much data needed for the modeling is available through its municipal energy company. The goal of the analyses is to understand how two different flexibility assets: namely a large heat pump coupled with a thermal storage system, and a fleet of electric vehicles equipped with smart charging, can aid in welcoming high shares of non controllable renewable energy sources (photovoltaic panels). This to avoid the feeding of a large share of electricity generation surplus to the national distribution system (thus increasing auto consumption) and to understand the impact on carbon emissions to gain a potential policy related insight
Empirical decision model learning
Full text linkOne of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. In this paper, we propose a methodology called Empirical Model Learning (EML) that relies on Machine Learning for obtaining components of a prescriptive model, using data either extracted from a predictive model or harvested from a real system. In a way, EML can be considered as a technique to merge predictive and prescriptive analytics. All models introduce some form of approximation. Citing G.E.P. Box [1] “Essentially, all models are wrong, but some of them are useful”. In EML, models are useful if they provide adequate accuracy, and if they can be effectively exploited by solvers for finding high-quality solutions. We show how to ground EML on a case study of thermal-aware workload dispatching. We use two learning methods, namely Artificial Neural Networks and Decision Trees and we show how to encapsulate the learned model in a number of optimization techniques, namely Local Search, Constraint Programming, Mixed Integer Non-Linear Programming and SAT Modulo Theories. We demonstrate the effectiveness of the EML approach by comparing our results with those obtained using expert-designed models
Temperature variation aware multi-scale delay, power and thermal analysis at RT and gate level
No full textThermal effects are rapidly gaining importance in nanometer CMOS technologies. Increased power density, coupled with spatio-temporal variability of chip workloads, causes on-die temperature non-uniformities. The assumption of a uniform temperature for the delay and power analysis of a large CMOS circuit produces inaccurate results. For this reason, significant design margins are taken to ensure safe operation. To improve design quality, we need precise localization of hotspots at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis needs to be done at multiple levels of granularity using a versatile thermal floorplan. We propose MiMAPT, an approach for analyzing delay, power and temperature in digital circuits. MiMAPT integrates seamlessly into major industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Thermal analysis is done at register-transfer (RT) and then gate-level considering non-regular shapes of on-die units with multiple scales of resolution and speed. To demonstrate the capability of MiMAPT in temperature variation aware delay/power estimation, a widely used IP block is chosen and four different chips are implemented using 65 nm and 40 nm (LVT, HVT) technology nodes. Different temperature patterns are then applied to the design. Accuracy improvements of up to 28% for static power and 16% for minimum clock period are reported in comparison with uniform averaged temperature assumption. Evaluating the ability of MiMAPT in multi-scale thermal analysis, a speed-up of 98× is reported compared to fine-grained method, while keeping false negatives at zero and the error of temperature estimation below 0.05 °C
An ultra-low power resilient multi-core architecture with static and dynamic tolerance to ambient temperature-induced variability
No full textNear-threshold operation is today a key research area in Ultra-Low Power (ULP) computing, as it prom-
ises a major boost in energy efficiency compared to super-threshold computing and it mitigates thermal
bottlenecks. Unfortunately near-threshold operation is plagued by greatly increased sensitivity to thresh-
old voltage variations, such as those caused by ambient temperature fluctuation. In this paper we focus
on a tightly-coupled ULP processor cluster architecture where a low latency, high-bandwidth processor-
to-L1-memory interconnection network plays a key role. We propose an architectural scheme to tolerate
ambient temperature-induced variations capable of statically (off-line) and dynamically (on-line) adapt-
ing the processor-to-L1-memory latency without compromising execution correctness. We extensively
tested our solution in different scenarios and we evaluated the different design trade-offs, showing the
cost, performance and reliability gain compared to state-of-the-art static solutions. The dynamic solution,
thanks to its lightweight runtime overhead, outperforms the static solution and is able to reach a perfor
mance gain up to 25% in a typical use case scenario with a very low (<4%) area overhead
Ekho: A 30.3W, 10k-Channel Fully Digital Integrated 3-D Beamformer for Medical Ultrasound Imaging Achieving 298M Focal Points per Second
No full text3-D medical ultrasound imaging enables new diagnostic possibilities and modalities. In a computational process called beamforming, a 3-D volume is reconstructed from several thousands of analog signals. Today's systems rely on massive analog preprocessing to reduce the computational burden of the subsequent digital processing system. In this paper, we present a configurable beamformer (BF) architecture, which demonstrates for the first time that it is possible to implement the entire 3-D delay and sum beamforming fully digitally and on one single chip, without requiring the off-chip memories. We present a presilicon implementation of a single-chip BF in an advanced 28-nm silicon-on-insulator technology. The BF targets a fully sampled 10k element 8-MHz bandwidth transducer head and is able to produce 298.1M focal points (FPs) per second - enough to produce a high-resolution volume with 16.3MFP at 15 Hz. All delays are computed online and on-chip to eliminate the power-hungry external memories for delay storage. The final design (register-transfer-level and floorplan) has a complexity of 342M gate equivalents requiring 1.68cm2 of area. The core power is estimated to be 30.3 W, resulting in an unprecedented power efficiency of 98.4G beamforming operations per watt
Assessing Tenstorrent’s RISC-V MatMul Acceleration Capabilities
No full textThe increasing demand for generative AI as Large Language
Models (LLMs) services has driven the need for specialized hardware ar-
chitectures that optimize computational e!ciency and energy consump-
tion. This paper evaluates the performance of the Tenstorrent Grayskull
e75 RISC-V accelerator for basic linear algebra kernels at reduced nu-
merical precision, a fundamental operation in LLM computations. We
present a detailed characterization of Grayskull’s execution model, grid
size, matrix dimensions, data formats, and numerical precision impact on
computational e!ciency. Furthermore, we compare Grayskull’s perfor-
mance against state-of-the-art architectures with tensor acceleration, in-
cluding Intel Sapphire Rapids processors and two NVIDIA GPUs (V100
and A100). Whilst NVIDIA GPUs dominate raw performance, Grayskull
demonstrates a competitive trade-o" between power consumption and
computational throughput, reaching a peak of 1.55 TFLOPs/Watt with
BF16
Energy Saving and Thermal Management Opportunities in a Workload-Aware MPI Runtime for a Scientific HPC Computing Node
No full textWith the advent of a new generation of supercomputers characterized by tightly-coupled integration of a large-number of powerful processing cores in the same die, energy and temperature walls are looming threats to the growth in computational power.
Scientific computing is characterized by a single application running in parallel on multiple nodes and cores until termination. The message-passing programming model is a widely adopted paradigm for explicitly handling data-sharing between processes of the same application. As an effect of the MPI communication patterns among different processes, the application is characterized by phases which can be exploited by OS power manager. In addition, the large number of cores integrated in the same silicon die introduces large thermal capacitance as well as on-die thermal heterogeneity. Jointly exploiting local workload unbalance and computational node heterogeneity can open interesting opportunities for advanced thermal and energy management. In this paper, we present an exploratory work to assess these opportunities and their limiting factors. We analyze application workload and we identify opportunities to reduce energy consumption and their impact on performance. We test our methodology on a widely-used quantum-chemistry application demonstrating potential benefits of combining the application flow with power and thermal management strategies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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