Google Colab CAD4U: Hands-On Cloud Laboratories for Digital Design

Canesche, Michael; Bragança, Lucas; Neto, Omar P. Vilela; Nacif, José Augusto M.; Ferreira, Ricardo

doi:10.1109/iscas51556.2021.9401151

Cited by 24 publications

(25 citation statements)

References 20 publications

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“…In Google Colab, all the tasks were done. Google Colab is a freely accessible cloud service for Free GPU [27]. We have developed deep learning coding with popular libraries including Keras, TensorFlow, PyTorch, OpenCV, and others.…”

Section: Data Retrieval and Methodologymentioning

confidence: 99%

Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

Ansari

Khan

2021

ELECTRON J GEN MED

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Section: Data Retrieval and Methodologymentioning

confidence: 99%

Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

Ansari

Khan

2021

ELECTRON J GEN MED

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“…Ferramentas como Jupyter Notebook [Rule et al 2018] vêm ganhando cada vez mais popularidade, principalmente para ensino de aprendizado de máquinas e áreas correlatas da Inteligência Artificial [Canesche and et al 2021]. O objetivo deste trabalho é utilizar a combinac ¸ão Jupyter Notebook + PYNQ como um ambiente interativo em um navegador, mostrando como usar a computac ¸ão com aceleradores em FPGAs com poucas linhas de código, abstraindo do usuário todo mecanismo complexo das ferramentas de projeto de hardware.…”

Section: Pynqunclassified

HPyC-FPGA - Integração de Aceleradores em FPGA de Alto Desempenho com Python para Jupyter Notebooks

Silva¹,

Penha²,

Ribeiro³

et al. 2022

Anais Do XXIII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2022)

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O desenvolvimento de aceleradores em FPGAs (Field Programmable Gates Arrays) ainda é um desafio. Recentemente, o ambiente PYNQ da Xilinx possibilitou a integração de código Python com aceleradores em FPGA. A maioria dos exemplos está voltada para placas de prototipação utilizadas no desenvolvimento de aplicações embarcadas. Este artigo apresenta o algoritmo K-means de aprendizado de máquina não supervisionado como estudo de caso. A principal contribuição deste trabalho é o encapsulamento de 3 aceleradores acoplados com PYNQ usando o ambiente Jupyter Notebook. A avaliação foi realizada em uma máquina de alto desempenho utilizando um FPGA Alveo U55C com memória HBM (High Bandwidth Memory). Os resultados são promissores, além de mostrar as facilidades de uso do FPGA de forma encapsulada, o ganho de desempenho foi de uma a duas ordens de grandezas em comparação a um sistema com dois processadores Xeon(R) Silver 4210R com 10 núcleos cada, executando a etapa de classificação do algoritmo K-means.

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“…With the proliferation of cloud computing and virtualisation, dedicated ML platforms and development environments are available to overcome the hardware and configuration issues (Carneiro, et al, 2018;Canesche, et al, 2021). Among them, Colaboratory (Colab) by Google, Azure Machine Learning by Microsoft and Watson Studio by IBM are prominent.…”

Section: Virtual Machines: Google Colabmentioning

confidence: 99%

A deep learning-based building defects detection tool for sustainability monitoring

Ekanayake¹

2022

Proceedings of 10th World Construction Symposium 2022

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To ensure sustainability of buildings, detection of building defects is crucial. Conventional practices of defects detection from building inspection data are mostly manual and error prone. With the advancements in computer vision, imaging technology and machine learning-based tools have been developed for real-time, accurate and efficient defects detection. Deep learning (DL), which is a branch of ML is more robust in automatically retrieving elements’ semantics to detect building defects. Although DL algorithms are robust in object detection, the computational complexities and configurations of these models are high. Therefore, this study presents a process of developing a computationally inexpensive and less complicated DL model using transfer learning and Google Colab virtual machine to improve automation in building defects detection. Cracks is one of the major building defects that constraint the safety and durability of buildings thus hindering building sustainability. Building cracks images were sourced from the Internet to train the model, which was built upon You Only Look Once (YOLO) DL algorithm. To test the DL model, inspection images of five (05) buildings collected by the Facilities Management department of a University in Sydney city were used. The DL model developed using this process offers a monitoring tool to ensure the sustainability of buildings with its’ ability of detecting cracks from building inspection data in real time accurately and efficiently. Although the current model is built to detect cracks, this process can be employed to automated detection of any building defect upon providing the training images of defects.

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Google Colab CAD4U: Hands-On Cloud Laboratories for Digital Design

Cited by 24 publications

References 20 publications

Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

HPyC-FPGA - Integração de Aceleradores em FPGA de Alto Desempenho com Python para Jupyter Notebooks

A deep learning-based building defects detection tool for sustainability monitoring

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