Google announces the open source of MobileNe : Primarily focus on optimizing for latency but also yield small networks. https://arxiv.org/abs/1704.04861
This material is to serve as guide reading of the paper.
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...Laxmi Kant Tiwari
Hi, In this lesson I will discuss how you can read a research paper and I will explain the MobileNet research paper published in 2017. I will first show you paper and the will present key findings through a .PPT presentation. I hope you would find it useful and like this video.
Learn Complete Data Science with these 5 video series.
1. Python for Beginners
https://www.youtube.com/watch?v=b42eTWkEIfA&list=PLc2rvfiptPSRmd4eWpRmzRIPebX3W9mju
2. Machine Learning for Beginners
https://www.youtube.com/watch?v=ZeM2tHtjGy4&list=PLc2rvfiptPSTvPFbNlT_TGRupzKKhJSIv
3. Feature Selection in Machine Learning
https://www.youtube.com/watch?v=kA4mD3y4aqA&list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH
4. Deep Learning with TensorFlow 2.0 and Keras
https://www.youtube.com/watch?v=nVvhkVLh60o&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF
5. Natural Language Processing (NLP) Tutorials
https://www.youtube.com/watch?v=mrF9MD56-wk&list=PLc2rvfiptPSQgsORc7iuv7UxhbRJox-pW&index=1
The working code is given in the video description of each video. You can download the Jupyter notebook from GitHub.
Please Like and Subscribe to show your support.
Like Facebook Page:
https://www.facebook.com/kgptalkie/
Make Your Own Automated Email Marketing Software in Python
https://www.youtube.com/watch?v=gmYuom6kfoY&list=PLc2rvfiptPSQK9ErKaLqf40iu1A3le9Zr
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/an-introduction-to-data-augmentation-techniques-in-ml-frameworks-a-presentation-from-amd/
Rajy Rawther, PMTS Software Architect at AMD, presents the “Introduction to Data Augmentation Techniques in ML Frameworks” tutorial at the May 2021 Embedded Vision Summit.
Data augmentation is a set of techniques that expand the diversity of data available for training machine learning models by generating new data from existing data. This talk introduces different types of data augmentation techniques as well as their uses in various training scenarios.
Rawther explores some built-in augmentation methods in popular ML frameworks like PyTorch and TensorFlow. She also discusses some tips and tricks that are commonly used to randomly select parameters to avoid having model overfit to a particular dataset.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/modern-machine-learning-from-basics-to-advanced-deep-learning-a-presentation-from-deep-netts/
Zoran Sevarac, Associate Professor at the University of Belgrade and Co-founder and CEO of Deep Netts, presents the “Modern Machine Vision from Basics to Advanced Deep Learning” tutorial at the May 2021 Embedded Vision Summit.
In this talk, Sevarac introduces the fundamentals of deep learning for image understanding. He begins by explaining the basics of convolutional neural networks (CNNs), and showing how CNNs are used to perform image classification and object detection. He provides an overview of the recent evolution of CNN topologies for object detection. He also illustrates typical use cases for CNN-based image classification and object detection, and provides a roadmap for getting started with deep learning for image understanding.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-sze
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Associate Professor at MIT, presents the "Approaches for Energy Efficient Implementation of Deep Neural Networks" tutorial at the May 2018 Embedded Vision Summit.
Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them.
This talk explores how DNNs are being mapped onto today’s processor architectures, and how these algorithms are evolving to enable improved efficiency. Sze explores the energy consumption of commonly used CNNs versus their accuracy, and provides insights on "energy-aware" pruning of these networks.
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...Laxmi Kant Tiwari
Hi, In this lesson I will discuss how you can read a research paper and I will explain the MobileNet research paper published in 2017. I will first show you paper and the will present key findings through a .PPT presentation. I hope you would find it useful and like this video.
Learn Complete Data Science with these 5 video series.
1. Python for Beginners
https://www.youtube.com/watch?v=b42eTWkEIfA&list=PLc2rvfiptPSRmd4eWpRmzRIPebX3W9mju
2. Machine Learning for Beginners
https://www.youtube.com/watch?v=ZeM2tHtjGy4&list=PLc2rvfiptPSTvPFbNlT_TGRupzKKhJSIv
3. Feature Selection in Machine Learning
https://www.youtube.com/watch?v=kA4mD3y4aqA&list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH
4. Deep Learning with TensorFlow 2.0 and Keras
https://www.youtube.com/watch?v=nVvhkVLh60o&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF
5. Natural Language Processing (NLP) Tutorials
https://www.youtube.com/watch?v=mrF9MD56-wk&list=PLc2rvfiptPSQgsORc7iuv7UxhbRJox-pW&index=1
The working code is given in the video description of each video. You can download the Jupyter notebook from GitHub.
Please Like and Subscribe to show your support.
Like Facebook Page:
https://www.facebook.com/kgptalkie/
Make Your Own Automated Email Marketing Software in Python
https://www.youtube.com/watch?v=gmYuom6kfoY&list=PLc2rvfiptPSQK9ErKaLqf40iu1A3le9Zr
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/an-introduction-to-data-augmentation-techniques-in-ml-frameworks-a-presentation-from-amd/
Rajy Rawther, PMTS Software Architect at AMD, presents the “Introduction to Data Augmentation Techniques in ML Frameworks” tutorial at the May 2021 Embedded Vision Summit.
Data augmentation is a set of techniques that expand the diversity of data available for training machine learning models by generating new data from existing data. This talk introduces different types of data augmentation techniques as well as their uses in various training scenarios.
Rawther explores some built-in augmentation methods in popular ML frameworks like PyTorch and TensorFlow. She also discusses some tips and tricks that are commonly used to randomly select parameters to avoid having model overfit to a particular dataset.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/modern-machine-learning-from-basics-to-advanced-deep-learning-a-presentation-from-deep-netts/
Zoran Sevarac, Associate Professor at the University of Belgrade and Co-founder and CEO of Deep Netts, presents the “Modern Machine Vision from Basics to Advanced Deep Learning” tutorial at the May 2021 Embedded Vision Summit.
In this talk, Sevarac introduces the fundamentals of deep learning for image understanding. He begins by explaining the basics of convolutional neural networks (CNNs), and showing how CNNs are used to perform image classification and object detection. He provides an overview of the recent evolution of CNN topologies for object detection. He also illustrates typical use cases for CNN-based image classification and object detection, and provides a roadmap for getting started with deep learning for image understanding.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-sze
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Associate Professor at MIT, presents the "Approaches for Energy Efficient Implementation of Deep Neural Networks" tutorial at the May 2018 Embedded Vision Summit.
Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them.
This talk explores how DNNs are being mapped onto today’s processor architectures, and how these algorithms are evolving to enable improved efficiency. Sze explores the energy consumption of commonly used CNNs versus their accuracy, and provides insights on "energy-aware" pruning of these networks.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/dnn-training-data-how-to-know-what-you-need-and-how-to-get-it-a-presentation-from-tech-mahindra/
Abhishek Sharma, Practice Head for Engineering AI at Tech Mahindra, presents the “DNN Training Data: How to Know What You Need and How to Get It” tutorial at the May 2021 Embedded Vision Summit.
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution.
In this talk, Sharma discusses approaches useful for situations where insufficient data is available, including transfer learning and data augmentation, including the use of generative adversarial networks (GANs). He also discusses techniques that can be helpful when data is plentiful, such as transforms, data path optimization and approximate computing. He illustrates these techniques and challenges via case studies from the healthcare and manufacturing industries.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-gormish
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Gormish, Research Manager at Clarifai, presents the "Machine Learning- based Image Compression: Ready for Prime Time?" tutorial at the May 2019 Embedded Vision Summit.
Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine- learned stages or end-to-end learning techniques where enough ground truth data is available.
Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine-learned compression techniques?
This talk examines practical aspects of deep learned image compression systems as compared with traditional approaches. Gormish considers memory, computation and other aspects, in addition to rate-distortion, to see when ML-based compression should be considered or avoided. He also discusses approaches using a combination of machine learned and traditional techniques.
Creating smaller, faster, production-ready mobile machine learning models.Jameson Toole
Dr. Jameson Toole - Cofounder and CTO of Fritz AI - https://www.fritz.ai
Abstract: Getting machine learning models ready for use on-device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. In this talk, you’ll learn optimization, pruning, and compression techniques that keep app sizes small and inference speeds high. We’ll apply these techniques using mobile machine learning frameworks such as Core ML and TensorFlow Lite.
Globe2Train: A Framework for Distributed ML Model Training using IoT Devices ...Bharath Sudharsan
Paper Pdf: https://www.researchgate.net/publication/356366494_Globe2Train_A_Framework_for_Distributed_ML_Model_Training_using_IoT_Devices_Across_the_Globe
Abstract:
Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing the idle hardware of numerous resource-constrained IoT devices existing across the globe. In such a global setting, staleness and real-world network uncertainties like congestion, latency, bandwidth issues are proven to impact the model convergence speed and training scalability. To implement the novel concept, while simultaneously addressing the real-world global distributed training challenges, we present Globe2Train (G2T), a framework with two components named G2T-Cloud (G2T-C) and G2T-Device (G2T-D) that can efficiently connect together multiple IoT devices and collectively train to produce the target ML models at very high speeds. The evaluation results with analysis show how the framework components jointly eliminate staleness and improve training scalability and speed by tolerating the real-world network uncertainties and by reducing the communication-to-computation ratio.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit.
The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform.
Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
PR-302: NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisHyeongmin Lee
드디어 PR12 Season 4가 시작되었습니다! 제가 이번 시즌에서 발표하게 된 첫 논문은 ""NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"라는 논문입니다. View Synthesis라는 Task는 몇 개의 시점에서 대상을 찍은 영상이 주어지면 주어지지 않은 위치와 방향에서 바라본 대상의 영상을 합성해내는 기술입니다. 이를 위해서 본 논문에서는 대상의 3D 정보를 통째로 Neural Network가 외우게 하는 방법을 선택했는데요, 이 방식은 Implicit Neural Representation이라는 이름으로 유명해지고 있는 추세고, 2D 이미지에 대해서도 적용하려는 접근들이 늘고 있습니다.
영상 링크: https://youtu.be/zkeh7Tt9tYQ
논문 링크: https://arxiv.org/abs/2003.08934
Yangqing Jia at AI Frontiers: Towards Better DL FrameworksAI Frontiers
The last few years has seen an abundance of deep learning and general machine learning frameworks, and such frameworks have created deep impacts to the machine learning industry. In this talk, Yangqing shares and discusses lessons we learned from building deep learning and general machine learning framework designs in the last few years, and share thoughts and philosophy in building the next generation of machine learning solutions for the AI industry. When applicable he draws examples from Caffe, a widely adopted deep learning framework that has evolved to serve computer vision, speech recognition, natural language understanding.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-hiremath-chou
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.
Next, the networks are trained using MATLAB’s GPU and parallel computing support either on the desktop, a local compute cluster or in the cloud. In the deployment phase, code generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/building-an-autonomous-detect-and-avoid-system-for-commercial-drones-a-presentation-from-iris-automation/
Alejandro Galindo, Head of Research and Development at Iris Automation, presents the “Building an Autonomous Detect-and-Avoid System for Commercial Drones” tutorial at the May 2021 Embedded Vision Summit.
Commercial and industrial drones have the potential to completely disrupt industries and create new ones. Used in applications such as infrastructure inspection, search and rescue, package delivery, and many others, they can save time, money, and lives. Most of these applications require a real-time understanding of the environment and the risks of collision.
At the same time, commercial drones are limited in the size, weight, and power they can carry, narrowing the options for sensors and computing architectures. In this presentation, Galindo dives into what it takes to build an autonomous detect-and-avoid system for commercial drones and, in particular, focuses on computer vision issues such as predictability and reduction of false positives. Why are they important and what does it take to drive them in the right direction?
Stochastic Computing Correlation Utilization in Convolutional Neural Network ...TELKOMNIKA JOURNAL
In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/dnn-training-data-how-to-know-what-you-need-and-how-to-get-it-a-presentation-from-tech-mahindra/
Abhishek Sharma, Practice Head for Engineering AI at Tech Mahindra, presents the “DNN Training Data: How to Know What You Need and How to Get It” tutorial at the May 2021 Embedded Vision Summit.
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution.
In this talk, Sharma discusses approaches useful for situations where insufficient data is available, including transfer learning and data augmentation, including the use of generative adversarial networks (GANs). He also discusses techniques that can be helpful when data is plentiful, such as transforms, data path optimization and approximate computing. He illustrates these techniques and challenges via case studies from the healthcare and manufacturing industries.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-gormish
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Gormish, Research Manager at Clarifai, presents the "Machine Learning- based Image Compression: Ready for Prime Time?" tutorial at the May 2019 Embedded Vision Summit.
Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine- learned stages or end-to-end learning techniques where enough ground truth data is available.
Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine-learned compression techniques?
This talk examines practical aspects of deep learned image compression systems as compared with traditional approaches. Gormish considers memory, computation and other aspects, in addition to rate-distortion, to see when ML-based compression should be considered or avoided. He also discusses approaches using a combination of machine learned and traditional techniques.
Creating smaller, faster, production-ready mobile machine learning models.Jameson Toole
Dr. Jameson Toole - Cofounder and CTO of Fritz AI - https://www.fritz.ai
Abstract: Getting machine learning models ready for use on-device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce aren’t small enough or fast enough to ship. In this talk, you’ll learn optimization, pruning, and compression techniques that keep app sizes small and inference speeds high. We’ll apply these techniques using mobile machine learning frameworks such as Core ML and TensorFlow Lite.
Globe2Train: A Framework for Distributed ML Model Training using IoT Devices ...Bharath Sudharsan
Paper Pdf: https://www.researchgate.net/publication/356366494_Globe2Train_A_Framework_for_Distributed_ML_Model_Training_using_IoT_Devices_Across_the_Globe
Abstract:
Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing the idle hardware of numerous resource-constrained IoT devices existing across the globe. In such a global setting, staleness and real-world network uncertainties like congestion, latency, bandwidth issues are proven to impact the model convergence speed and training scalability. To implement the novel concept, while simultaneously addressing the real-world global distributed training challenges, we present Globe2Train (G2T), a framework with two components named G2T-Cloud (G2T-C) and G2T-Device (G2T-D) that can efficiently connect together multiple IoT devices and collectively train to produce the target ML models at very high speeds. The evaluation results with analysis show how the framework components jointly eliminate staleness and improve training scalability and speed by tolerating the real-world network uncertainties and by reducing the communication-to-computation ratio.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit.
The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform.
Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
PR-302: NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisHyeongmin Lee
드디어 PR12 Season 4가 시작되었습니다! 제가 이번 시즌에서 발표하게 된 첫 논문은 ""NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"라는 논문입니다. View Synthesis라는 Task는 몇 개의 시점에서 대상을 찍은 영상이 주어지면 주어지지 않은 위치와 방향에서 바라본 대상의 영상을 합성해내는 기술입니다. 이를 위해서 본 논문에서는 대상의 3D 정보를 통째로 Neural Network가 외우게 하는 방법을 선택했는데요, 이 방식은 Implicit Neural Representation이라는 이름으로 유명해지고 있는 추세고, 2D 이미지에 대해서도 적용하려는 접근들이 늘고 있습니다.
영상 링크: https://youtu.be/zkeh7Tt9tYQ
논문 링크: https://arxiv.org/abs/2003.08934
Yangqing Jia at AI Frontiers: Towards Better DL FrameworksAI Frontiers
The last few years has seen an abundance of deep learning and general machine learning frameworks, and such frameworks have created deep impacts to the machine learning industry. In this talk, Yangqing shares and discusses lessons we learned from building deep learning and general machine learning framework designs in the last few years, and share thoughts and philosophy in building the next generation of machine learning solutions for the AI industry. When applicable he draws examples from Caffe, a widely adopted deep learning framework that has evolved to serve computer vision, speech recognition, natural language understanding.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-hiremath-chou
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.
Next, the networks are trained using MATLAB’s GPU and parallel computing support either on the desktop, a local compute cluster or in the cloud. In the deployment phase, code generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/from-inference-to-action-ai-beyond-pattern-recognition-a-keynote-presentation-from-pieter-abbeel/
Professor Pieter Abbeel, Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab, presents the “From Inference to Action: AI Beyond Pattern Recognition” tutorial at the May 2021 Embedded Vision Summit.
Pattern recognition—such as that used in image recognition, speech recognition and machine translation—has been the primary focus of the last decade’s progress in artificial intelligence. But intelligence fundamentally requires more than mere pattern recognition: It also requires the ability to achieve goal-oriented behaviors. Two new methods, deep reinforcement learning and deep imitation learning, provide paradigms for learning goal-oriented behaviors and have shown great promise in recent research. These approaches have demonstrated remarkable success in learning to play video games, learning to control simulated and real robots, mastering the classical game of Go and automation of character animation.
In this talk, Abbeel describes the ideas underlying these advances, and their current capabilities and limitations, with a focus on practical applications. He explores the characteristics that have unlocked important new use cases (e.g. AI robotic automation in warehouses) while others (e.g., self-driving cars) remain AI-bottlenecked. He also highlights important areas where significant breakthroughs are still needed.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/building-an-autonomous-detect-and-avoid-system-for-commercial-drones-a-presentation-from-iris-automation/
Alejandro Galindo, Head of Research and Development at Iris Automation, presents the “Building an Autonomous Detect-and-Avoid System for Commercial Drones” tutorial at the May 2021 Embedded Vision Summit.
Commercial and industrial drones have the potential to completely disrupt industries and create new ones. Used in applications such as infrastructure inspection, search and rescue, package delivery, and many others, they can save time, money, and lives. Most of these applications require a real-time understanding of the environment and the risks of collision.
At the same time, commercial drones are limited in the size, weight, and power they can carry, narrowing the options for sensors and computing architectures. In this presentation, Galindo dives into what it takes to build an autonomous detect-and-avoid system for commercial drones and, in particular, focuses on computer vision issues such as predictability and reduction of false positives. Why are they important and what does it take to drive them in the right direction?
Stochastic Computing Correlation Utilization in Convolutional Neural Network ...TELKOMNIKA JOURNAL
In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.
Rendering Process of Digital Terrain Model on Mobile DevicesWaqas Tariq
Digital Terrain Model has been used in many applications especially in Geographical Information System applications. However with the recently improved mobile devices that can support 3 Dimension (3D) content, rendering 3D based terrain on mobile devices is possible. Although mobile devices have improved its capabilities, rendering 3D terrain is tedious due to the constraint in resources of mobile devices. Furthermore, rendering DTM add more constraint and issues to the mobile devices. This paper focuses on the rendering processes of DTM on mobile devices to observe some issues and current constraints occurred. Also to determined the characteristic of terrain properties that will affect the rendering performance. Experiments were performed using five datasets that derived from aerial images. The experimental results are based on speed of rendering and the appearance of the terrain surface. From these results, issues and problems that highlighted in this paper will be the focus of future research.
Takes the reader through the various components of windowing systems, and how to develop and benchmark various Graphics applications using OpenGL and other toolsets. Also includes a Cheatsheet that covers various terminologies used in the Graphics world.
A N A LTERNATIVE G REEN S CREEN K EYING M ETHOD F OR F ILM V ISUAL E ...ijma
This study focuses on a green screen keying method
developed especially for film visual effects. There
are a
series of ways of using existing tools for creating
mattes from green or blue screen plates. However,
it is
still a time-consuming process, and the results var
y especially when it comes to retaining tiny detail
s, such
as hair and fur. This paper introduces an alternati
ve concept and method for retaining edge details of
characters on a green screen plate, also, a number
of connected mathematical equations are explored. A
t
the end of this study, a simplified process of appl
ying this method in real productions is also tested
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
Energy efficiency is one of the most critical issue in design of System on Chip. In Network On
Chip (NoC) based system, energy consumption is influenced dramatically by mapping of
Intellectual Property (IP) which affect the performance of the system. In this paper we test the
antecedently extant proposed algorithms and introduced a new energy proficient algorithm
stand for 3D NoC architecture. In addition a hybrid method has also been implemented using
bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has
been implemented and evaluated on randomly generated benchmark and real life application
such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing algorithm (spiral and crinkle) and has shown better
reduction in the communication energy consumption and shows improvement in the
performance of the system. Comparing our work with spiral and crinkle, experimental result
shows that the average reduction in communication energy consumption is 19% with spiral and
17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21%
whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work
and the existing methods using bio-inspired technique and having the comparison among them
an average energy reduction is found to be of 18% and 24%.
ENERGY AND LATENCY AWARE APPLICATION MAPPING ALGORITHM & OPTIMIZATION FOR HOM...cscpconf
Energy efficiency is one of the most critical issue in design of System on Chip. In Network On
Chip (NoC) based system, energy consumption is influenced dramatically by mapping of
Intellectual Property (IP) which affect the performance of the system. In this paper we test the
antecedently extant proposed algorithms and introduced a new energy proficient algorithm
stand for 3D NoC architecture. In addition a hybrid method has also been implemented using
bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has
been implemented and evaluated on randomly generated benchmark and real life application
such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing algorithm (spiral and crinkle) and has shown better
reduction in the communication energy consumption and shows improvement in the
performance of the system. Comparing our work with spiral and crinkle, experimental result
shows that the average reduction in communication energy consumption is 19% with spiral and
17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21%
whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work
and the existing methods using bio-inspired technique and having the comparison among them
an average energy reduction is found to be of 18% and 24%.
NTT Laboratories
J. Arai, S. Yagi, H. Uchiyama, T. Honjo, T. Inagaki, K. Inaba, T. Ikuta, H. Takesue, K. Horikawa
This material is a poster exhibited at the ITBL community booth in SC19 (The International Conference for High Performance Computing, Networking, Storage, and Analysis 2019).
In today’s world the growing demand for knowledge has made cloud computing a center of attraction. Cloud computing is providing utility based services to all the users worldwide. It enables presentation of applications from consumers, scientific and business domains. However, data centers created for cloud computing applications consume huge amounts of energy, contributing to high operational costs and a large amount of carbon dioxide emission to the environment. With enhancement of data center, the power consumption is increasing at such a rate that it has become a key concern these days because it is ultimately leading to energy shortcomings and global climatic change. Therefore, we need green cloud computing solutions that can not only save energy, but also reduce operational costs.
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx
Introducing google’s mobile nets
1. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 1/8
Introducing Google’s MobileNets
( by Larry Guo tcglarry@gmail.com)
The following Material is introduction of Paper:
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Google)
https://arxiv.org/abs/1704.04861
Objective
a class of efficient models called MobileNets for mobile and embedded vision applications
Motivation:
General Trend of CNN - Getting Deeper and Complicated to get higher accuracy
However, not improve in size and speed
In many real world applications, , the recognition tasks need to be carried out in a timely fashion on
a computation- ally limited platform.
MobileNets:
Primarily focus on optimizing for latency but also yield small networks.
MobileNet Architecture(Depthwise Separable Convolution)
D is the Keneral Size
M: Input Channel
N: Output Chanel
Traditional CNN : Kernel Size: [D , D , M] *N (Feature Maps)
Issues: Resulting to High Computation Cost
K
K K
2. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 2/8
MobileNet (depthwise Separable Convolution)
Stage1: Depthwise Convolution - Using Kernel size [D , D , 1] *M(Feature Maps)
Stage2: Pointwise Convolution - Using (Stage1Output) with Kernel Size [1, 1, M]*N(Feature Maps)
Combine the above 2 operation will get ‘similar result’ as traditional CNN with significantly lower
computation cost to ( )
Left: Traditional CNN Layer; Right: MobileNet Layer
Actual Network Architecture
K K
N
1
DK
2
1
3. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 3/8
Note: Most Computation in 1x1 Convolution: Cna be used GEMM (general matrix multiply) to
Accelerate
Downsizing Methodology
α ∈ (0, 1]: reduce the feature maps size αM, αN, (in the paper 1, 0.75,0.5,0.25) (reduce
computation roughly by α
ρ ∈ (0, 1], change the input resolution 224, 192, 160, 128 (reduce computation by ρ
Results (Comparison of Hyper Parameter Setting):
2
2
4. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 4/8
Result of Depthwise Separable Convolution vs Full Convolution
with sacrifice of 1% accuracy, the computation cost decreases significantly!
Cut width or Cut Depth ? (shallow = the 5 layers of separable filters with feature size 14 × 14 × 512
in Table 1 are removed), 0.75 = 0.75*M feature maps Cut width is better !!!
Comparison of different width (accuracy vs computation cost), SAME resolution
Comparison of different resolution
5. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 5/8
Log Linear dependency between Accuracy and Multi Adds
Accuracy vs Number of Parameters
Results vs Popular Model
vs GoogleNet, VGG16
6. 2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 6/8
Smaller MobileNet vs SquuezeNet(for a smaller Network) and AlexNet
Fine Grained Recognition (Stanford Dogs)
Face Attributes Classification
![2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 1/8
Introducing Google’s MobileNets
( by Larry Guo tcglarry@gmail.com)
The following Material is introduction of Paper:
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Google)
https://arxiv.org/abs/1704.04861
Objective
a class of efficient models called MobileNets for mobile and embedded vision applications
Motivation:
General Trend of CNN - Getting Deeper and Complicated to get higher accuracy
However, not improve in size and speed
In many real world applications, , the recognition tasks need to be carried out in a timely fashion on
a computation- ally limited platform.
MobileNets:
Primarily focus on optimizing for latency but also yield small networks.
MobileNet Architecture(Depthwise Separable Convolution)
D is the Keneral Size
M: Input Channel
N: Output Chanel
Traditional CNN : Kernel Size: [D , D , M] *N (Feature Maps)
Issues: Resulting to High Computation Cost
K
K K](https://image.slidesharecdn.com/introducinggooglesmobilenets-170716073941/85/Introducing-google-s-mobile-nets-1-320.jpg)
![2017/7/16 Introducing Google’s MobileNets
https://paper.dropbox.com/doc/print/ObfURZ1vmZcZMGs0zN9zo?print=true 2/8
MobileNet (depthwise Separable Convolution)
Stage1: Depthwise Convolution - Using Kernel size [D , D , 1] *M(Feature Maps)
Stage2: Pointwise Convolution - Using (Stage1Output) with Kernel Size [1, 1, M]*N(Feature Maps)
Combine the above 2 operation will get ‘similar result’ as traditional CNN with significantly lower
computation cost to ( )
Left: Traditional CNN Layer; Right: MobileNet Layer
Actual Network Architecture
K K
N
1
DK
2
1](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)