Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, pro ling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how e ciently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations

JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution

Recent years have witnessed a rapid growth of deep-network based services and applications. A practical and critical problem thus has emerged: how to effectively deploy the deep neural network models such that they can be executed efficiently. Conventional cloud-based approaches usually run the deep models in data center servers, causing large latency because a significant amount of data has to be transferred from the edge of network to the data center. In this paper, we propose JALAD, a joint accuracy- and latency-aware execution framework, which decouples a deep neural network so that a part of it will run at edge devices and the other part inside the conventional cloud, while only a minimum amount of data has to be transferred between them. Though the idea seems straightforward, we are facing challenges including i) how to find the best partition of a deep structure; ii) how to deploy the component at an edge device that only has limited computation power; and iii) how to minimize the overall execution latency. Our answers to these questions are a set of strategies in JALAD, including 1) A normalization based in-layer data compression strategy by jointly considering compression rate and model accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall execution latency; and 3) An edge-cloud structure adaptation strategy that dynamically changes the decoupling for different network conditions. Experiments demonstrate that our solution can significantly reduce the execution latency: it speeds up the overall inference execution with a guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE

Sistema de reconocimiento de personas que están usando la mascarilla incorrectamente en espacios públicos

La presente investigación incorporó el desarrollo y la implementación de un sistema de reconocimiento de personas que están usando la mascarilla incorrectamente en espacios públicos, debido a que nos encontramos en un momento crucial a causa del nivel de contagio de COVID-19 y al poco respeto por las normas de bioseguridad entre la población lo cual ha generado lamentablemente llegar a un nivel crítico. Se tuvo como objetivo contestar la interrogante: ¿Qué efecto tuvo el uso del sistema de reconocimiento personas que usan la mascarilla incorrectamente en la identificación, tiempo de entrenamiento y uso de recursos de PC? y teniendo como final el desarrollo de un método que permita la mejora en la detección de personas que no estén usando de forma correcta la mascarilla y a su vez tener un control más riguroso. Para la construcción del sistema nos apoyamos en el lenguaje de programación Python. Este estudio según su propósito es aplicado, con un enfoque cuantitativo, y con el diseño pre experimental el cual incluyó 848 imágenes de personas que estaban usando la mascarilla facial tanto de forma correcta e incorrecta para el entrenamiento y para la muestra un conjunto de 135 imágenes de personas en establecimientos públicos. Se utilizó la ficha de registro para recopilar datos en cuanto a precisión y tiempos de reconocimiento. Los resultados fueron beneficiosos para esta investigación, logrando incrementar la precisión en comparación con un modelo tradicional en un 17%. Se recomendó ampliar este estudio mediante el entrenamiento de imágenes en un ordenador con mejores recursos y utilizar una cámara CCTV para poder capturar mejor los datos de imagen