Deep Learning Use Cases using OpenPOWER systems

1. The document discusses the top 5 stories highlighting what's hot in high performance computing (HPC) and artificial intelligence (AI). 2. The stories include a tool that makes it easier to create HPC application containers, how to understand deep learning performance, the White House hosting an AI summit, a call for submissions on interactive HPC workflows, and a supercomputing center adding Nvidia GPUs to expand their cloud services. 3. The top stories provide insights on the latest tools, events, and technologies in HPC and AI.

AIDC NY: Applications of Intel AI by QuEST Global - 09.19.2019

Intel® Software

QuEST Global is a global engineering company that provides AI and digital transformation services using technologies like computer vision, machine learning, and deep learning. It has developed several AI solutions using Intel technologies like OpenVINO that provide accelerated inferencing on Intel CPUs. Some examples include a lung nodule detection solution to help detect early-stage lung cancer from CT scans and a vision analytics platform used for applications in retail, banking, and surveillance. The company leverages Intel's AI Builder program and ecosystem to develop, integrate, and deploy AI solutions globally.

Deep learning @ Edge using Intel's Neural Compute Stick

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Distributed deep learning reference architecture v3.2l

This document provides an overview of IBM's reference architecture for deep learning clusters. It discusses the hardware and software components, including POWER-based servers with NVIDIA GPUs connected by Mellanox InfiniBand switches. It describes the storage architecture using IBM Spectrum Scale for a shared filesystem. The software stack is based on Red Hat Enterprise Linux, CUDA, Nvidia-Docker, IBM PowerAI, and container orchestration with either Kubernetes or IBM Spectrum LSF. Operational models and workflows are shown to support experimentation, scaling, and production phases of deep learning.

OpenPOWER Boot camp in Zurich

The document discusses IBM PowerAI LMS and DDL. PowerAI LMS allows deep learning models and data to utilize both GPU and system memory for processing. This enables support for larger models and higher resolution data. PowerAI DDL is a communication library that enables distributed deep learning across multiple servers with GPUs. It works with frameworks like TensorFlow to improve scaling efficiency compared to other solutions. A demo is then presented on the performance benefits of PowerAI LMS and DDL.

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This document summarizes a cytoscape team meeting on May 8, 2014. It discusses the OpenVis conference, which brings together practitioners in visualization including developers, designers, and analysts. The keynote speakers were introduced, including Mike Bostock who created the D3.js library. Bostock's talk focused on how D3 works and its use of data-driven documents to create interactive visualizations in web browsers. The document notes that while cytoscape uses Java for desktop apps, web technologies like cytoscape.js should be used for sharing data. It relates D3 and the team's projects, suggesting D3 could be used to visualize the cytoscape design process from Git commits.

ISC 2016 Day 2 Recap

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WML OpenPOWER presentation

The document provides an overview of Watson Machine Learning Community Edition (WML-CE), an open source machine learning and deep learning platform from IBM. WML-CE includes frameworks like TensorFlow, PyTorch, and Caffe alongside IBM contributions like Large Model Support (LMS), Distributed Deep Learning (DDL), and SnapML. SnapML is a set of libraries that accelerate popular machine learning models across CPU and GPU in a distributed manner. The document highlights key SnapML features and performance advantages over other frameworks.

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Python Data Science and Machine Learning at Scale with Intel and Anaconda

Intel® Software

Python is the number 1 language for data scientists, and Anaconda is the most popular python platform. Intel and Anaconda have partnered to bring scalability and near-native performance to Python with simple installations. Learn how data scientists can now access oneAPI-optimized Python packages such as NumPy, Scikit-Learn, Modin, Pandas, and XGBoost directly from the Anaconda repository through simple installation and minimal code changes.

Vertex Perspectives | AI Optimized Chipsets | Part IV

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Harnessing the virtual realm

Alison B. Lowndes

NVIDIA is working on tackling climate change through the development of digital twins of Earth using AI and high performance computing. They are collaborating with various partners on initiatives like Destination Earth, which envisions an interactive digital twin platform for modeling and simulation. NVIDIA technologies like Omniverse, AI, and upcoming CPUs like Grace could help make a fully realized digital twin a reality. This would allow researchers to better understand climate systems and explore different scenarios to help mitigate and adapt to climate change.

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This document summarizes a project to develop a home surveillance vehicle using Arduino. The vehicle uses an Arduino MEGA, camera module, motors and WiFi to provide remote monitoring. OpenPose deep learning is used to recognize poses and detect falls. The model was trained on a dataset of 5,200 images split between training and testing. It achieved 96% accuracy at distinguishing between falls, walking and sitting. The system aims to assist elderly home monitoring and address safety issues.

Deep Learning Applications and Image Processing

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With the rapid development of digital technologies, the analysis and processing of data has become an important problem. In particular, classification, clustering and processing of complex and multi structured data required the development of new algorithms. In this process, Deep Learning solutions for solving Big Data problems are emerging. Deep Learning can be described as an advanced variant of artificial neural networks. Deep Learning algorithms are commonly used in healthcare, facial and voice recognition, defense, security and autonomous vehicles. Image processing is one of the most common applications of Deep Learning. Deep Learning software is commonly used to capture and process images by removing the errors. Image processing methods are used in many fields such as medicine, radiology, military industry, face recognition, security systems, transportation, astronomy and photography. In this study, current Deep Learning algorithms are investigated and their relationship with commonly used software in the field of image processing is determined. Ahmet Ã–zcan | Mahmut Ãœnver | Atilla ErgÃ¼zen "Deep Learning Applications and Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49142.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49142/deep-learning-applications-and-image-processing/ahmet-Ã¶zcan

Mask Detection and Temperature Sensor System.pptx

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Due to the COVID-19 pandemic, wearing a mask is mandatory in public spaces, as properly wearing a mask offers a maximum preventive effect against viral transmission. Body temperature has also become an important consideration in determining whether an individual is healthy. In this work, we design a real-time deep learning model to meet the current demand to detect the mask-wearing position and head temperature of a person before he or she enters a public space. In this experiment, we use a deep learning object detection method to create a mask position and head temperature detector. We implement an RGB camera and thermal camera to generate input images and capture a person's temperature respectively. The output of these experiments is a live video that carries accurate information about whether a person is wearing a mask properly and what his or her head temperature or palm temperature.

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This document is a 36-page bachelor's thesis written by Duc Minh Luong Nguyen titled "Detect COVID-19 from Chest X-Ray images using Deep Learning". The thesis was submitted to Metropolia University of Applied Sciences in May 2020. It aims to build a deep convolutional neural network to detect COVID-19 using only chest X-ray images. The model achieves an accuracy of 93% at detecting COVID-19 patients versus healthy patients, despite being trained on a small dataset of 115 images for each class.

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This document describes an autonomous entry system that uses a Raspberry Pi, temperature sensor, and camera to detect people's temperatures and whether they are wearing a mask. It aims to prevent the spread of COVID-19. The system works by using a contactless temperature sensor to check temperature and a Pi camera to detect masks. If someone has a high temperature or no mask, their photo is emailed. The system could be implemented in public areas to reduce virus transmission and human effort. It provides autonomous temperature scanning and mask detection to allow entry for people who are masked and don't have a fever.

SCREENLESS DISPLAY.pptx

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Screenless displays are an emerging technology that allows information to be displayed without a physical screen. There are three main types: visual images using holograms or projections onto intermediate objects; retinal displays that project images directly onto the retina; and synaptic interfaces that transmit visual information directly to the brain. Some advantages are lower power needs, higher resolution, and greater portability compared to traditional screens. Challenges include high costs currently and the technology still being in development. Many companies are working to advance screenless displays for uses like augmented reality glasses and new generations of televisions without physical screens.

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IRJET- Object Detection in an Image using Convolutional Neural Network

This document summarizes a research paper on object detection in images using convolutional neural networks. The paper proposes using the ImageAI Python library along with a region-based convolutional neural network model to perform object detection. The methodology loads a pre-trained model, imports the ImageAI detection class, detects objects in an input image, and prints the detected objects and probabilities. The system can accurately detect objects like faces and vehicles from images and has applications in security and traffic monitoring. However, training the neural network requires a large dataset and takes a long time.

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Eyes of Things

This document summarizes the Eyes of Things (EoT) project, which aims to develop an open hardware and software platform for computer vision applications. The platform will be low-cost, small, and optimized for computer vision tasks. It will allow developers to easily create vision-based applications. Four demonstration applications will be developed, including surveillance, augmented reality, cloud computing, and perceptual computing. The platform is being developed to address challenges with existing devices for "out of factory" computer vision applications related to size, cost, and flexibility. It utilizes the Movidius Myriad 2 processor and Awaiba NanEye camera. The software includes computer vision functions, middleware, and APIs to control the device from a smartphone or

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This document describes a system for intelligent helmet detection using OpenCV and machine learning. The system uses a camera to capture video of a person's face in real-time. Each video frame is preprocessed using OpenCV and fed to a machine learning model trained on the YOLO algorithm to detect whether a helmet is present. If a helmet is detected, an Arduino board connected to the system will not activate a buzzer or turn on an LED, otherwise it will. The goal is to help enforce helmet usage and reduce fatal injuries from motorcycle accidents. Key components include the camera, Arduino, buzzer, LED, TensorFlow for the ML model, OpenCV for preprocessing, and Darkflow which implements YOLO in Python using TensorFlow

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The Libre-SOC Project aims to create an entirely Libre-Licensed, transparently-developed fully auditable Hybrid 3D CPU-GPU-VPU, using the Supercomputer-class OpenPOWER ISA as the foundation. Our first test ASIC is a 180nm "Fixed-Point" Power ISA v3.0B processor, 5.1mm x 5.9mm, as a proof-of-concept for the team, whose primary expertise is in Software Engineering. Software Engineering training brings a radically different approach to Hardware development: extensive unit tests, source code revision control, automated development tools are normal. Libre Project Management brings even more: bug trackers, mailing lists, auditable IRC logs and a wiki are standard fare for Libre Projects that are simply not normal Industry-Standard practice. This talk therefore goes through the workflow, from the original HDL through to the GDS-II layout, showing how we were able to keep track of the development that led to the IMEC 180nm tape-out in July 2021. In particular, by following a parallel development process involving "Real" and "Symbolic" Cell Libraries, developed by Chips4Makers, will be shown how our developers did not need to sign a Foundry NDA, but were still able to work side-by-side with a University that did. With this parallel development process, the University upheld their NDA obligations, and Libre-SOC were simultaneously able to honour its Transparency Objectives.

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IT Industry is going through two major transformations. One is adaption of AI and tight integration of the same in the commercial applications and enterprise workflow. Two the transformation in software architecture through the concepts like microservices and the cloud native architecture. These transformation alongside the aggressive adaption of IoT/mobile and 5G in all our day today activities is making the world operate in more real time manner which opens-up a new challenge to improve the hardware architecture to adapt to these requirements. These above two major transformation pushes the boundary of the entire systems stack making the designer rethink hardware. This talk presents you a picture of how the enterprise Industry leading POWER architecture is transforming to fulfill the performance demands of these newer generation workloads with primary focus on the AI acceleration on the chip.

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Everything is changing from Health Care to the Automotive markets without forgetting Financial markets or any type of engineering everything has stopped being created as an individual or best-case scenario a team effort to something that is being developed and perfectioned by using AI and hundreds of computers.And even AI is something that we no longer can run in a single computer, no matter how powerful it is. What drives everything today is HPC or High-Performance Computing heavily linked to AI In this session we will discuss about AI, HPC computing, IBM Power architecture and how it can help develop better Healthcare, better Automobiles, better financials and better everything that we run on them

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Healthcare has became one of the most important aspects of everyones life. Its importance has surged due to the latests outbreaks and due to this latest pandemic it has become mandatory to collaborate to improve everyones Healthcare as soon as possible. IBM has reacted quickly sharing not only its knowledge but also its Artificial Intelligence Supercomputers all around the world. Those Supercomputers are helping to prevail this outbreak and also future ones. They have completely different features compared to proposals from other players of this Supercomputers market. We will try to make a quick look at the differences of those AI focused Supercomputers and how they can help in the R&D of Healthcare solutions for everyone, from those ones with access to a big IBM AI Supercomputer to those ones with access to only one small IBM AI focused server.

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