FPGA accelerator for gradient boosting decision trees

A decision tree is a well-known machine learning technique. Recently their popularity has increased due to the powerful Gradient Boosting ensemble method that allows to gradually increasing accuracy at the cost of executing a large number of decision trees. In this paper we present an accelerator designed to optimize the execution of these trees while reducing the energy consumption. We have implemented it in an FPGA for embedded systems, and we have tested it with a relevant case-study: pixel classification of hyperspectral images. In our experiments with different images our accelerator can process the hyperspectral images at the same speed at which they are generated by the hyperspectral sensors. Compared to a high-performance processor running optimized software, on average our design is twice as fast and consumes 72 times less energy. Compared to an embedded processor, it is 30 times faster and consumes 23 times less energy

A novel approach for adapting the standard addition method to single particle-ICP-MS for the accurate determination of NP size and number concentration in complex matrices; 35414390

This paper presents a novel approach, based on the standard addition method, for overcoming the matrix effects that often hamper the accurate characterization of nanoparticles (NPs) in complex samples via single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). In this approach, calibration of the particle size is performed by two different methods: (i) by spiking a suspension of NPs standards of known size containing the analyte, or (ii) by spiking the sample with ionic standards; either way, the measured sensitivity is used in combination with the transport efficiency (TE) for sizing the NPs. Moreover, such transport efficiency can be readily obtained from the data obtained via both calibration methods mentioned above, so that the particle number concentration can also be determined. The addition of both ionic and NP standards can be performed on-line, by using a T-piece with two inlet lines of different dimensions. The smaller of the two is used for the standards, thus ensuring a constant and minimal sample dilution. As a result of the spiking of the samples, mixed histograms including the signal of the sample and that of the standards are obtained. However, the use of signal deconvolution approaches permits to extract the information, even in cases of signal populations overlapping. For proofing the concept, characterization of a 50 nm AuNPs suspension prepared in three different media (i.e., deionized water, 5% ethanol, and 2.5% tetramethyl ammonium hydroxide-TMAH) was carried out. Accurate results were obtained in all cases, in spite of the matrix effects detected in some media. Overall, the approach proposed offers flexibility, so it can be adapted to different situations, but it might be specially indicated for samples for which the matrix is not fully known and/or dilution is not possible/recommended. © 2022 The Author

Analysis of a Pipelined Architecture for Sparse DNNs on Embedded Systems

Deep neural networks (DNNs) are increasing their presence in a wide range of applications, and their computationally intensive and memory-demanding nature poses challenges, especially for embedded systems. Pruning techniques turn DNN models into sparse by setting most weights to zero, offering optimization opportunities if specific support is included. We propose a novel pipelined architecture for DNNs that avoids all useless operations during the inference process. It has been implemented in a field-programmable gate array (FPGA), and the performance, energy efficiency, and area have been characterized. Exploiting sparsity yields remarkable speedups but also produces area overheads. We have evaluated this tradeoff in order to identify in which scenarios it is better to use that area to exploit sparsity, or to include more computational resources in a conventional DNN architecture. We have also explored different arithmetic bitwidths. Our sparse architecture is clearly superior on 32-bit arithmetic or highly sparse networks. However, on 8-bit arithmetic or networks with low sparsity it is more profitable to deploy a dense architecture with more arithmetic resources than including support for sparsity. We consider that FPGAs are the natural target for DNN sparse accelerators since they can be loaded at run-time with the best-fitting accelerator

Evaluation of the changes in working limits in an automobile assembly line using simulation

The aim of the work presented in this paper consists of the development of a decision-making support system, based on discrete-event simulation models, of an automobile assembly line which was implemented within an Arena simulation environment and focused at a very specific class of production lines with a four closed-loop network configuration. This layout system reflects one of the most common configurations of automobile assembly and preassembly lines formed by conveyors. The sum of the number of pallets on the intermediate buffers, remains constant, except for the fourth closed-loop, which depends on the four-door car ratio (x) implemented between the door disassembly and assembly stations of the car body. Some governing equations of the four closed-loops are not compatible with the capacities of several intermediate buffers for certain values of variable x. This incompatibility shows how the assembly line cannot operate in practice for x0,97 in a stationary regime, due to the starvation phenomenon or the failure of supply to the machines on the production line. We have evaluated the impact of the pallet numbers circulating on the first closed-loop on the performance of the production line, translated into the number of cars produced/hour, in order to improve the availability of the entire manufacturing system for any value of x. Until the present date, these facts have not been presented in specialized literature. © 2012 American Institute of Physics

Performance and energy efficiency analysis of a Reversi player for FPGAs and General Purpose Processors

Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained. Since the Artificial Intelligence techniques applied in these games are computationally intensive, the applications developed for mobile systems are frequently simplistic, far from the level of equivalent applications developed for desktop computers. Currently board games are software applications executed on General Purpose Processors. However, they exhibit a medium degree of parallelism and a custom hardware accelerator implemented on an FPGA can take advantage of that. We have selected the well-known Reversi game as a case study because it is a very popular board game with simple rules but huge computational demands. We developed and optimized software and hardware designs for this game that apply the same classical Artificial Intelligence techniques. The applications have been executed on different representative platforms and the results demonstrate that the FPGAs implementations provide better performance, lower power consumption and, therefore, impressive energy savings. These results demonstrate that FPGAs can efficiently deal with this kind of problems

Improving accuracy on wave height estimation through machine learning techniques

Estimatabion of wave agitation plays a key role in predicting natural disasters, path optimization and secure harbor operation. The Spanish agency Puertos del Estado (PdE) has several oceanographic measure networks equipped with sensors for different physical variables, and manages forecast systems involving numerical models. In recent years, there is a growing interest in wave parameter estimation by using machine learning models due to the large amount of oceanographic data available for training, as well as its proven efficacy in estimating physical variables. In this study, we propose to use machine learning techniques to improve the accuracy of the current forecast system of PdE. We have focused on four physical wave variables: spectral significant height, mean spectral period, peak period and mean direction of origin. Two different machine learning models have been explored: multilayer perceptron and gradient boosting decision trees, as well as ensemble methods that combine both models. These models reduce the error of the predictions of the numerical model by 36% on average, demonstrating the potential gains of combining machine learning and numerical models

Multi-scale effects of nestling diet on breeding performance in a terrestrial top predator inferred from stable isotope analysis

This is the final version of the article. Available from the publisher via the DOI in this record.Inter-individual diet variation within populations is likely to have important ecological and evolutionary implications. The diet-fitness relationships at the individual level and the emerging population processes are, however, poorly understood for most avian predators inhabiting complex terrestrial ecosystems. In this study, we use an isotopic approach to assess the trophic ecology of nestlings in a long-lived raptor, the Bonelli's eagle Aquila fasciata, and investigate whether nestling dietary breath and main prey consumption can affect the species' reproductive performance at two spatial scales: territories within populations and populations over a large geographic area. At the territory level, those breeding pairs whose nestlings consumed similar diets to the overall population (i.e. moderate consumption of preferred prey, but complemented by alternative prey categories) or those disproportionally consuming preferred prey were more likely to fledge two chicks. An increase in the diet diversity, however, related negatively with productivity. The age and replacements of breeding pair members had also an influence on productivity, with more fledglings associated to adult pairs with few replacements, as expected in long-lived species. At the population level, mean productivity was higher in those population-years with lower dietary breadth and higher diet similarity among territories, which was related to an overall higher consumption of preferred prey. Thus, we revealed a correspondence in diet-fitness relationships at two spatial scales: territories and populations. We suggest that stable isotope analyses may be a powerful tool to monitor the diet of terrestrial avian predators on large spatio-temporal scales, which could serve to detect potential changes in the availability of those prey on which predators depend for breeding. We encourage ecologists and evolutionary and conservation biologists concerned with the multi-scale fitness consequences of inter-individual variation in resource use to employ similar stable isotope-based approaches, which can be successfully applied to complex ecosystems such as the Mediterranean.Funding for this work was provided by projects CGL2007-64805 and CGL2010-17056 from the ‘‘Ministerio de Ciencia e Innovacio´n, Gobierno de Espan˜ a’’, the ‘‘A`rea d’Espais Naturals de la Diputacio´ de Barcelona’’, and Miquel Torres S.A. Fieldwork in France was carried out within the framework of the second National Action Plan for Bonelli’s eagle from the ‘‘Ministe`re franc¸ais de l’E´cologie, de L’E´nergie, du De´veloppement Durable et de la Mer’’ and coordinated by the DREAL LR ‘‘Direction Re´gionale de l’Environnement, de l’Ame´nagement et du Logement-Languedoc-Roussillon’’. J. Resano-Mayor was supported by a predoctoral grant from the ‘‘Departamento de Educacio´n, Gobierno de Navarra; Plan de Formacio´n y de I+D 2008–2009’’, and M. Moleo´n by a postdoctoral grant from the ‘‘Ministerio de Educacio´n, Gobierno de Espan˜ a; Plan Nacional de I+D+i 2008–2011’’. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Simultaneous determination of V, Ni, Ga and Fe in fuel fly ash using solid sampling high resolution continuum source graphite furnace atomic absorption spectrometry

A green and simple method has been proposed in this work for the simultaneous determination of V, Ni, Ga and Fe in fuel ash samples by solid sampling high resolution continuum source graphite furnace atomic absorption spectrometry (SS HR CS GFAAS). The application of fast programs in combination with direct solid sampling allows eliminating pretreatment steps, involving minimal manipulation of sample. Iridium treated platforms were applied throughout the present study, enabling the use of aqueous standards for calibration. Correlation coefficients for the calibration curves were typically better than 0.9931. The concentrations found in the fuel ash samples analyzed ranged from 0.66 to 4.2 % for V, 0.23 to 0.7 % for Ni, 0 to 5.4 mg/Kg for Ga and 0.10 to 0.60 % for Fe. Precision (%RSD) were 5.2, 10.0, 20.0 and 9.8% for V, Ni, Ga and Fe, respectively, obtained as the average of the %RSD of six replicates of each fuel ash sample. The optimum conditions established were applied to the determination of the target analytes in fuel ash samples. In order to test the accuracy and applicability of the proposed method in the analysis of samples, five ash samples from the combustion of fuel in power stations, were analysed. The method accuracy was evaluated by comparing the results obtained using the proposed method with the results obtained by ICP OES previous acid digestion. The results showed good agreement between them

Performance, carcass characteristics, economic margin and meat quality in young Tudanca bulls fed on two levels of grass silage and concentrate

This study investigates the effect on performance, carcass and meat characteristics of increasing the forage level in the diet of fattening Tudanca young bulls using silage as the forage source as compared with a conventional ad libitum straw plus concentrate diet. Twenty two Tudanca young bulls were assigned to three different finishing diets: ad libitum grass silage plus ad libitum concentrate (GS-AC), ad libitum grass silage plus concentrate limited to a half of the intake of the ad libitum group (GS-LC), ad libitum barley straw plus ad libitum concentrate (Str-AC) and then slaughtered at around 11 months of age. GS-LC diet resulted in relation to GS-AC and Str-AC diets in lower (p <= 0.05) average daily weight gain (750 vs 1, 059 and 991 g/animal/day, respectively), lower (p <= 0.05) carcass weight (133 vs 159 and 152 kg, respectively) and carcasses with slightly lower conformation scores. Although GS-LC diet allowed for a lower dependence on concentrate (372 vs 657 and 729 kg/animal, respectively), economic margin was similar for the two GS groups (-63.1 and -64.1 vs -91.8 (sic)/head). The polyunsaturated/saturated fatty acid ratio was the lowest (p <= 0.05) in GS-AC meat (the group showing the highest IMF levels) and the ratio n-6/n-3 was the highest (p <= 0.05) in Str-AC meat. GS-LC meat showed higher collagen content and Str-AC meat presented higher cohesiveness, springiness and chewiness values. Results suggested that the substitution of straw by grass silage and the restriction of the concentrate level could be recommended for finishing young Tudanca bulls in indoors systems

Glossary of methods and terms used in analytical spectroscopy (IUPAC Recommendations 2019)

Recommendations are given concerning the terminology of concepts and methods used in spectroscopy in analytical chemistry, covering nuclear magnetic resonance spectroscopy, atomic spectroscopy, and vibrational spectroscopy. © 2021 IUPAC and De Gruyter. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. For more information, please visit: http://creativecommons.org/licenses/by-nc-nd/4.0/ 2021