L'aiuto all'agricoltura, ovvero progetto per la istituzione in Italia delle casse di prestiti agricoli ipotecari funzionanti eziandio da casse di depositi e risparmi / per Eugenio Stabile

L'aiuto all'agricoltura, ovvero progetto per la istituzione in Italia delle casse di prestiti agricoli ipotecari funzionanti eziandio da casse di depositi e risparmi / per Eugenio Stabile Roma : Tip. alle terme diocleziane, 1882 24 p. ; 24 cm

Optimización de cálculo con OpenCL para sistemas de entrenamiento de redes neuronales

[ES] El campo relacionado con la inteligencia artificial ha supuesto una revolución tanto en la industria como en la sociedad, mejorando la calidad de vida y la productividad del trabajo. Aun así, existen ciertas áreas en las cuales los dispositivos que utilizan esta tecnología necesitan de un uso eficiente de sus componentes para hacer posible el uso de la inteligencia artificial en entornos prácticos. Por esto, el motivo de este trabajo final de grado es la optimización relativa al coste computacional de las funciones previamente creadas en OpenCL para dar soporte a los dispositivos GPUs en la aplicación HELENNA, la cual realiza el entrenamiento e inferencia de redes neuronales. Como resultado obtenemos una reducción considerable del tiempo de ejecución en las distintas redes neuronales tanto densamente conectadas como convolucionales.[EN] Artificial intelligence has been a revolution in both industry and society, improving quality of life and work productivity. But, there are certain areas in which devices that use this technology require efficient use of their components to make possible the use of artificial intelligence in real-world environments. So, the motivator for this work is the optimization of the computational cost of functions previously created in OpenCL to support GPU devices in the HELENNA application. The HELENNA application performs training and inference on neural networks. As a result of these optimizations, we obtain a considerable reduction in execution time in both fully connected and convolutional neural networks.[CA] El camp relacionat amb la intel·ligència artificial ha suposat una revolució tant en la indústria com en la societat, millorant la qualitat de vida i la productivitat del treball. Tanmateix, existeixen certes àrees en les quals els dispositius que utilitzen aquesta tecnologia necessiten un ús eficient dels seus components per a fer possible l’ús de la intel·ligència artificial en entorns pràctics. Llavors, el motiu d’aquest treball final de grau és l’optimització relativa al cost computacional de les funcions prèviament creades en OpenCL per a donar suport als dispositius GPUs en l’aplicació HELENNA, la qual realitza l’entrenament i la inferència de xarxes neuronals. Com a resultat obtenim una reducció considerable del temps d’execució de les diferents xarxes neuronals tan densament connectades com convolucionals.Stabile, EB. (2020). Optimización de cálculo con OpenCL para sistemas de entrenamiento de redes neuronales. http://hdl.handle.net/10251/151954TFG

Optimización del producto matricial sobre dispositivos de bajo consumo para inferencia en Deep Learning

[ES] El aprendizaje automático mediante redes neuronales profundas ha experimentado un gran auge en la última década, principalmente por la combinación de varios factores, entre los que se incluyen la avalancha de datos para entrenar este tipo de sistemas (big data), una mayor capacidad de los sistemas de computación (procesadores gráficos de NVIDIA, TPUs de Google, etc.), los avances en técnicas algorítmicas de aprendizaje (por ejemplo, redes de tipo transformer para procesamiento del lenguaje), y la disponibilidad de entornos amigables para la tarea. En la actualidad existen diferentes paquetes de software para el entrenamiento de redes neuronales profundas sobre clusters de computadores (TensorFlow de Google y PyTorch de Facebook), e incluso los mismos paquetes tienen versiones especializadas (TensorFlow Lite, NVIDIA RT, QNNPACK, etc.) para realizar el proceso de inferencia sobre procesadores de bajo consumo, como los que pueden encontrarse en un móvil Android o iOS o en un vehículo sin conductor. Muchos de los sistemas tratan redes neuronales convolucionales, especialmente aquellos que tratan con imágenes. A un nivel más bajo de detalle podemos observar que el entrenamiento y la inferencia en las capas convolucionales de las redes neuronales mencionadas aparece un producto matricial con características particulares, bien definidas y que requieren de un tratamiento especial cuando se trata de su optimización. Este trabajo de fin de máster trata de la optimización de esta operación, en particular, sobre arquitectura ARM, cuyo procesador multinúcleo puede encontrarse en gran parte de los dispositivos de bajo consumo donde se pretende ejecutar la inferencia de una red previamente entrenada. La optimización planteada está inspirado en un paquete de rutinas optimizadas de álgebra lineal numérica denominado BLIS, de donde se obtienen los algoritmos básicos sobre los que se realiza el trabajo. El proyecto permitirá al estudiante adquirir un buen conocimiento de los aspectos computacionales relacionados con el proceso inferencia con redes neuronales profundas, así como profundizar en la interacción entre el algoritmo y la arquitectura del procesador y cómo esta determina el rendimiento.[EN] The use of machine learning in deep neural networks has experienced a boom in the last decade, mainly due to a combination of several factors, including the abundance of data to train such systems (big data), increased computing power (NVIDIA graphics processors, Google TPUs, etc.), advances in algorithmic learning techniques (transformer neural networks for language processing) and the availability of user-friendly environments for the task. There are currently different software packages for training deep neural networks on computer clusters (TensorFlow and PyTorch) and even the same packages have specialized versions (TensorFlow Lite, NVIDIA RT, QNNPACK, etc.) to perform the inference process on low-power processors, such as those that can be found in an Android or iOS mobile phone or in a driverless car. Many of the systems deal with convolutional neural networks, especially those that deal with images. At a lower level of detail, we can observe that the training and inference in the convolutional layers of the aforementioned neural networks result in a matrix product with particular, well-defined characteristics that require special treatment when it comes to optimization. This master's thesis deals with the optimization of this operation, in particular, on an ARM architecture, whose multicore processor can be found in most of the low-power devices where it is intended to execute the inference of a previously trained network. The proposed optimization is inspired by a package of optimized numerical linear algebra routines called BLIS, from which the basic algorithms on which the work is carried out are obtained. The project will allow the student to acquire a good knowledge of the computational aspects related to the inference process with deep neural networks, as well as to deepen the interaction between the algorithm and the architecture of the processor and how this determines the performance.Stabile, EB. (2021). Optimización del producto matricial sobre dispositivos de bajo consumo para inferencia en Deep Learning. Universitat Politècnica de València. http://hdl.handle.net/10251/172885TFG

Very late bioresorbable scaffold thrombosis and reoccurrence of dissection two years later chronic total occlusion recanalization of the left anterior descending artery

We describe the case of a patient presenting with ST-segment elevation myocardial infarction due to very late scaffold thrombosis. The patient was already admitted for an elective percutaneous recanalization of a chronically occluded left anterior descending artery (LAD). The procedure was performed according the sub-intimal tracking and re-entry (STAR) technique with 4 bioresorbable vascular scaffolds implantation. However, even though the coronary flow was preserved at the end of the procedure, the dissected segment was only partially sealed at the distal segment of the LAD. After 18 mo of regular assumption, dual antiplatelet therapy was discontinued for 10 mo before his presentation at the emergency room. This is the first reported case of a very late scaffold thrombosis after coronary chronic total occlusion (CTO) recanalization performed according to the STAR technique. This case raises concerns about the risk of very late scaffold thrombosis after complex CTO revascularization

Effects of balloon injury on neointimal hyperplasia in steptozotocin-induced diabetes and in hyperinsulinemic nondiabetic pancreatic islet-transplanted rats.

BACKGROUND: The mechanisms of increased neointimal hyperplasia after coronary interventions in diabetic patients are still unknown. METHODS AND RESULTS: Glucose and insulin effects on in vitro vascular smooth muscle cell (VSMC) proliferation and migration were assessed. The effect of balloon injury on neointimal hyperplasia was studied in streptozotocin-induced diabetic rats with or without adjunct insulin therapy. To study the effect of balloon injury in nondiabetic rats with hyperinsulinemia, pancreatic islets were transplanted under the kidney capsule in normal rats. Glucose did not increase VSMC proliferation and migration in vitro. In contrast, insulin induced a significant increase in VSMC proliferation and migration in cell cultures. Furthermore, in VSMC culture, insulin increased MAPK activation. A reduction in neointimal hyperplasia was consistently documented after vascular injury in hyperglycemic streptozotocin-induced diabetic rats. Insulin therapy significantly increased neointimal hyperplasia in these rats. This effect of hyperinsulinemia was totally abolished by transfection on the arterial wall of the N17H-ras-negative mutant gene. Finally, after experimental balloon angioplasty in hyperinsulinemic nondiabetic islet-transplanted rats, a significant increase in neointimal hyperplasia was observed. CONCLUSIONS: In rats with streptozotocin-induced diabetes, balloon injury was not associated with an increase in neointimal formation. Exogenous insulin administration in diabetic rats and islet transplantation in nondiabetic rats increased both blood insulin levels and neointimal hyperplasia after balloon injury. Hyperinsulinemia through activation of the ras/MAPK pathway, rather than hyperglycemia per se, seems to be of crucial importance in determining the exaggerated neointimal hyperplasia after balloon angioplasty in diabetic animals