This work proposes a parallel algorithm for computing the arithmetic reduction of n numbers as a set of matrix-multiply accumulate (MMA) operations that are executed simultaneously by GPU tensor cores. The analysis, assuming tensors of size m m, shows that the proposed algorithm has a parallel running time of $T (n) = 5\;\log_{m^2}(n)$ and a speedup of $S = (5/4)\;\log_2 (m^2)$ over a canonical parallel reduction. Experimental performance results on a Tesla V100 GPU show that the tensor-core based approach is energy efficient and runs up to 3.2 and 2 faster than a standard GPU-based reduction and Nvidias CUB library, respectively, while keeping the numerical error below 1% with respect to a double precision CPU reduction. The chained design of the algorithm allows a flexible configuration of GPU thread-blocks and the optimal values found through experimentation agree with the theoretical ones. The results obtained in this work show that GPU tensor cores are relevant not only for Deep Learning or Linear Algebra computations, but also for applications that require the acceleration of large summations.

Photovoltaic cells, based on quantum dots implementation in the intrinsic region, are one of the most widely studied concepts nowadays to obtain a high solar conversion efficiency. The challenge in this third generation of solar cells is to find a good combination of materials that allows obtaining higher efficiency with low cost. In this study, we consider a juxtaposition of two kinds of quantum dots (dot/barrier) inside the I region of the PIN junction: the first combination of semiconductors includes the two configurations, PbTe/CdTe and PbSe/ZnTe, and the second combination is InN/GaN. Thus the intermediate band can be tailored by controlling the size of the dots and the inter-dot distances. The principal interest of this investigation is to determine the optimized parameters (the dot size and the inter-dot distance), leading to obtain a better solar cell efficiency. Intermediate bands, their positions, and their widths, are determined using 3D confined particles (electron and hole). Their energy levels are determined by solving the Schrödinger equation and solving the well-known dispersion relation in the Kronig–Penney model.

Finding the best configuration of a neural network’s hyper-parameters may take too long to be feasible using an exhaustive search, especially when the cardinality of the search space has a big combinatorial number of possible solutions with various hyper-parameters. This problem is aggravated when we also need to optimize the parameters of the neural network, such as the weight of the hidden neurons and biases. Extreme learning machines (ELMs) are part of the random weights neural network family, in which parameters are randomly initialized, and the solution, unlike gradient-descent-based algorithms, can be found analytically. This ability is especially useful for metaheuristic analysis due to its reduced training times allowing a faster optimization process, but the problem of finding the best hyper-parameter configuration is still remaining. In this paper, we propose a modification of the artificial bee colony (ABC) metaheuristic to act as parameterizers for a regularized ELM, incorporating three methods: an adaptive mechanism for ABC to balance exploration (global search) and exploitation (local search), an adaptation of the opposition-based learning technique called opposition local-based learning (OLBL) to strengthen exploitation, and a record of access to the search space called forbidden redundant indexes (FRI) that allow us to avoid redundant calculations and track the explored percentage of the search space. We set ten parameterizations applying different combinations of the proposed methods, limiting them to explore up to approximately 10% of the search space, with results over 98% compared to the maximum performance obtained in the exhaustive search in binary and multiclass datasets. The results demonstrate a promising use of these parameterizations to optimize the hyper-parameters of the R-ELM in datasets with different characteristics in cases where computational efficiency is required, with the possibility of extending its use to other problems with similar characteristics with minor modifications, such as the parameterization of support vector machines, digital image filters, and other neural networks, among others.

We have studied a route of chaos in the dissipative Landau–Lifshitz-Gilbert equation representing the magnetization dynamics of an anisotropic nanoparticle subjected to a time-variant magnetic field. This equation presents interesting chaotic dynamics. In the parameter space, for some forcing frequency and magnetic strength of the applied field, one observes a transition from a regular periodic behavior to chaotic dynamics. The chaotic dynamics, close to the bifurcation, are characterized by type-III intermittency. Long epochs of quasi-regular dynamics followed by turbulent bursts. The characterization of the intermittencies has been done through four different techniques. The first method is associated with the computation of the Lyapunov exponents that characterize the chaotic regime. The second and third methods are associated with the statistics of the duration of the laminar epochs prior to a turbulent burst. The fourth method is associated with the subharmonic instability present in those laminar epochs and quantified through a Poincaré section method. At the end of the manuscript, we compare the result obtained by the different techniques and discuss the methods’ limitations.

This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of machine learning (ML) models, using the optimal variant among different Extreme Learning Machine (ELM) models. This tool represents a concrete contribution to the use of artificial intelligence in smart agriculture, enabling more efficient and accessible management of cherry tree crops. The overall goal is to evaluate machine learning-based strategies that enable efficient and low-computational-cost detection of the disease, facilitating its implementation on devices with limited resources. The ERICA database is used by following a proper methodology in order to learning and validation stages are completely independent. Preprocessing includes renaming, cropping, scaling, grayscale conversion, vectorization, and normalization. Subsequently, the impact of reducing image resolution is studied, identifying that a size of 63 × 23 pixels maintains a good balance between visual detail and computational efficiency. Six ELM variants are trained: standard, regularized (R-ELM), class-weighted (W1-ELM and W2-ELM), and multilayer (ML2-ELM and ML3-ELM), and classical machine learning approaches are optimized and compared with classical ML approaches. The results indicate that W1-ELM achieves the best performance among tested variants, reaching an accuracy of 0.77 and a geometric mean of 0.45 with a training time in order of seconds.