For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/optimization-techniques-with-intels-openvino-to-enhance-performance-on-your-existing-hardware-a-presentation-from-intel/
Nico Galoppo, Principal Engineer (substituting for Ansley Dunn, Product Marketing Manager), and Ryan Loney, Technical Product Manager, both of Intel, present the “Optimization Techniques with Intel’s OpenVINO to Enhance Performance on Your Existing Hardware” tutorial at the May 2022 Embedded Vision Summit.
Whether you’re using TensorFlow, PyTorch or another framework, Galoppo and Loney show you optimization techniques to enhance performance on your existing hardware. With the OpenVINO Toolkit, built on the foundation of OneAPI, developers can utilize their own AI model or leverage one of the hundreds of pre-trained models available across vision and audio use cases.
In this presentation, you’ll learn how the Neural Network Compression Framework provides optimal model training templates for performance boosts while preserving accuracy, and how the Model Optimizer reduces complexity and makes model conversion faster. Other areas explored by Galoppo and Loney include auto device discovery to enable automatic load balancing and how to optimize for latency or throughput based on your workload.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-gorbachev
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yury Gorbachev, Principal Engineer at Intel, presents the "How to Get the Best Deep Learning Performance with the OpenVINO Toolkit" tutorial at the May 2019 Embedded Vision Summit.
Tremendous recent progress in deep learning and computer vision algorithms has made it possible to create innovative applications that were not previously feasible. However, moving from academic research to real-world algorithm deployment is still complicated due to the amount of native programming and low-level knowledge that is required to unleash the full performance of processing platforms.
This talk demonstrates how the Intel OpenVINO toolkit makes it easy to move deep learning algorithms from research to deployment. Gorbachev walks through the most important toolkit features that allow you to create lightweight applications and reach maximum performance on various processing platforms, including traditional CPUs as well as accelerators such as VPUs, GPUs and FPGAs.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/hailo/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Orr Danon, CEO of Hailo, presents the "Emerging Processor Architectures for Deep Learning: Options and Trade-offs" tutorial at the May 2019 Embedded Vision Summit.
In the past year, numerous new processor architectures for machine learning have emerged. Many of these focus on edge applications, reflecting the growing demand for deploying machine learning outside of data centers. This intensive focus on processor architecture innovation comes at a perfect time in light of the slowing progress in silicon fabrication technology and the massive opportunities for deployment of AI applications using vision and other sensors.
In this presentation, Danon explores the architectural concepts underlying these diverse processors and analyzes their suitability for various applications. He derives the performance bounds of each architecture approach and provides insights on the practical deployment of machine learning using these specialized architectures. In addition, using a case study, he explores the opportunities enabled through designing neural networks to exploit specialized processor architectures.
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/optimization-techniques-with-intels-openvino-to-enhance-performance-on-your-existing-hardware-a-presentation-from-intel/
Nico Galoppo, Principal Engineer (substituting for Ansley Dunn, Product Marketing Manager), and Ryan Loney, Technical Product Manager, both of Intel, present the “Optimization Techniques with Intel’s OpenVINO to Enhance Performance on Your Existing Hardware” tutorial at the May 2022 Embedded Vision Summit.
Whether you’re using TensorFlow, PyTorch or another framework, Galoppo and Loney show you optimization techniques to enhance performance on your existing hardware. With the OpenVINO Toolkit, built on the foundation of OneAPI, developers can utilize their own AI model or leverage one of the hundreds of pre-trained models available across vision and audio use cases.
In this presentation, you’ll learn how the Neural Network Compression Framework provides optimal model training templates for performance boosts while preserving accuracy, and how the Model Optimizer reduces complexity and makes model conversion faster. Other areas explored by Galoppo and Loney include auto device discovery to enable automatic load balancing and how to optimize for latency or throughput based on your workload.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/intel/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-gorbachev
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Yury Gorbachev, Principal Engineer at Intel, presents the "How to Get the Best Deep Learning Performance with the OpenVINO Toolkit" tutorial at the May 2019 Embedded Vision Summit.
Tremendous recent progress in deep learning and computer vision algorithms has made it possible to create innovative applications that were not previously feasible. However, moving from academic research to real-world algorithm deployment is still complicated due to the amount of native programming and low-level knowledge that is required to unleash the full performance of processing platforms.
This talk demonstrates how the Intel OpenVINO toolkit makes it easy to move deep learning algorithms from research to deployment. Gorbachev walks through the most important toolkit features that allow you to create lightweight applications and reach maximum performance on various processing platforms, including traditional CPUs as well as accelerators such as VPUs, GPUs and FPGAs.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/hailo/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Orr Danon, CEO of Hailo, presents the "Emerging Processor Architectures for Deep Learning: Options and Trade-offs" tutorial at the May 2019 Embedded Vision Summit.
In the past year, numerous new processor architectures for machine learning have emerged. Many of these focus on edge applications, reflecting the growing demand for deploying machine learning outside of data centers. This intensive focus on processor architecture innovation comes at a perfect time in light of the slowing progress in silicon fabrication technology and the massive opportunities for deployment of AI applications using vision and other sensors.
In this presentation, Danon explores the architectural concepts underlying these diverse processors and analyzes their suitability for various applications. He derives the performance bounds of each architecture approach and provides insights on the practical deployment of machine learning using these specialized architectures. In addition, using a case study, he explores the opportunities enabled through designing neural networks to exploit specialized processor architectures.
The content was modified from Google Content Group
Eric ShangKuan(ericsk@google.com)
---
TensorFlow Lite guide( for mobile & IoT )
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size.
TensorFlow Lite consists of two main components:
The TensorFlow Lite interpreter:
- optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers.
The TensorFlow Lite converter:
- which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance.
---
Event: PyLadies TensorFlow All-Around
Date: Sep 25, 2019
Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/
Linkedin: http://linkedin.com/in/mia-chang/
As virtualization technology becomes pervasive there is a continuing demand to increase the performance of guest virtual machines. Many hardware virtualization techniques, such as nested paging and IOMMU, have already been developed to accelerate the guest virtual machines frequent operations in different areas. However, one area that has not yet been addressed is the handling of interrupts in a virtual machine environment.
This presentation talks about the design of AMD virtual interrupt controller (AVIC). The AVIC architecture addresses the overhead of interrupt processing in a virtualized environment by applying hardware acceleration to three major components of interrupt processing: 1) Delivery of interrupts directly from I/O devices to a guest operating system; 2) Interprocessor interrupts between the virtual CPUs in a guest; 3) Local APIC accesses by guest operating systems.
Not all gameplay needs to happen immediately. In fact, there are many cases in which deferring commands may offer a better outcome – improved user experience, performance, etc. These slides explore thinking about where deferred commands are needed and provides examples on how to take full advantage of the Entity Command Buffer.
Speaker: Elora Krzanich – Unity
Watch the session on YouTube: https://youtu.be/SecJibpoTYw
Part 01 Linux Kernel Compilation (Ubuntu)Tushar B Kute
Presentation on "Linux Kernel Compilation" (Ubuntu based).
Presented at Army Institute of Technology, Pune for FDP on "Basics of Linux Kernel Programming". by Tushar B Kute (http://tusharkute.com).
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/vitis-and-vitis-ai-application-acceleration-from-cloud-to-edge-a-presentation-from-xilinx/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Vinod Kathail, Fellow and Chief Architect at Xilinx, presents the “Vitis and Vitis AI: Application Acceleration from Cloud to Edge” tutorial at the September 2020 Embedded Vision Summit.
Xilinx SoCs and FPGAs provide significant advantages in throughput, latency, and energy efficiency for production deployments of compute-intensive applications when compared to CPUs and GPUs. Over the last decade, FPGAs have evolved into highly configurable devices that provide on-chip heterogeneous multi-core CPUs, domain-specific programmable accelerators and “any-to-any” interface connectivity.
Today, the Xilinx Vitis Unified Software Platform supports high-level programming in C, C++, OpenCL, and Python, enabling developers to build and seamlessly deploy applications on Xilinx platforms including Alveo cards, FPGA instances in the cloud, and embedded devices. Moreover, Vitis enables the acceleration of large-scale data processing and machine learning applications using familiar high-level frameworks, such as TensorFlow and SPARK. This presentation provides an overview of the Vitis Software platform and the accelerated Vitis Vision Library, which enables customizable functions such as image signal processing, adaptable AI inference, 3D reconstruction and motion analysis.
This is a talk at AI Nextcon Seattle on Feb 12, 2020.
An overview of TensorFlow Lite and various resources for helping you deploy TFLite models to mobile and edge devices. Walk through an example of end to end on-device ML: train a model from scratch, convert to TFLite and deploy it.
Overview of the Linux Kernel, based on "Anatomy of the Linux Kernel" by M. Tim Jones, (IBM Developerworks) http://www.ibm.com/developerworks/linux/library/l-linux-kernel/
Best Practices For Game Development Using Perforce Streams Perforce
To build a future hit, AAA game development teams need to manage a complex environment. Making a game involves a lot of (big) files, many contributors, and millions of changes. The sheer number of branches associated can be overwhelming for any team.
That’s why 19 of the top 20 game development studios choose Helix Core –– version control from Perforce.
Take Sumo Digital. They use Helix Core to manage obstacles, visualize code, and integrate the tools they need. And they use Perforce Streams –– branching and merging in Helix Core –– to guide development and streamline their workflows.
Join Mark Washbrook and Tony Crowther from Sumo Digital, along with Chuck Gehman from Perforce, to learn:
-Key version control challenges for AAA game development.
-What is Perforce Streams?
-How Sumo Digital uses Perforce Streams to integrate with Unreal.
Discover how your team can benefit from using Streams.
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/tensorflow-lite-for-microcontrollers-tflm-recent-developments-a-presentation-from-bdti-and-google/
David Davis, Senior Embedded Software Engineer, and John Withers, Automation and Systems Engineer, both of BDTI, present the “TensorFlow Lite for Microcontrollers (TFLM): Recent Developments” tutorial at the May 2022 Embedded Vision Summit.
TensorFlow Lite Micro (TFLM) is a generic inference framework designed to run TensorFlow models on digital signal processors (DSPs), microcontrollers and other embedded targets with small memory footprints and very low power usage. TFLM aims to be easily portable to various embedded targets from those running an RTOS to bare-metal code. TFLM leverages the model optimization tools from the TensorFlow ecosystem and has additional embedded-specific optimizations to reduce the memory footprint. TFLM also integrates with a number of community contributed optimized hardware-specific kernel implementations.
In this talk, Davis and Withers review collaboration between BDTI and Google over the last year, including porting nearly two dozen operators from TensorFlow Lite to TFLM, creation of a separate Arduino examples repository, improved testing and documentation of both Arduino and Colab training examples and transitioning TFLM’s open-source CI framework to use GitHub Actions.
Bring Intelligence to the Edge with Intel® Movidius™ Neural Compute StickDESMOND YUEN
Motiviation to move intelligence to the edge
Edge compute use cases
Barriers to moving intelligence to the edge
Deep learning algorithms – can they run on an edge device?
Movidius Neural Compute Stick (arch,usage, etc)
As virtualization technology becomes pervasive there is a continuing demand to increase the performance of guest virtual machines. Many hardware virtualization techniques, such as nested paging and IOMMU, have already been developed to accelerate the guest virtual machines frequent operations in different areas. However, one area that has not yet been addressed is the handling of interrupts in a virtual machine environment.
This presentation talks about the design of AMD virtual interrupt controller (AVIC). The AVIC architecture addresses the overhead of interrupt processing in a virtualized environment by applying hardware acceleration to three major components of interrupt processing: 1) Delivery of interrupts directly from I/O devices to a guest operating system; 2) Interprocessor interrupts between the virtual CPUs in a guest; 3) Local APIC accesses by guest operating systems.
Not all gameplay needs to happen immediately. In fact, there are many cases in which deferring commands may offer a better outcome – improved user experience, performance, etc. These slides explore thinking about where deferred commands are needed and provides examples on how to take full advantage of the Entity Command Buffer.
Speaker: Elora Krzanich – Unity
Watch the session on YouTube: https://youtu.be/SecJibpoTYw
Part 01 Linux Kernel Compilation (Ubuntu)Tushar B Kute
Presentation on "Linux Kernel Compilation" (Ubuntu based).
Presented at Army Institute of Technology, Pune for FDP on "Basics of Linux Kernel Programming". by Tushar B Kute (http://tusharkute.com).
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/vitis-and-vitis-ai-application-acceleration-from-cloud-to-edge-a-presentation-from-xilinx/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Vinod Kathail, Fellow and Chief Architect at Xilinx, presents the “Vitis and Vitis AI: Application Acceleration from Cloud to Edge” tutorial at the September 2020 Embedded Vision Summit.
Xilinx SoCs and FPGAs provide significant advantages in throughput, latency, and energy efficiency for production deployments of compute-intensive applications when compared to CPUs and GPUs. Over the last decade, FPGAs have evolved into highly configurable devices that provide on-chip heterogeneous multi-core CPUs, domain-specific programmable accelerators and “any-to-any” interface connectivity.
Today, the Xilinx Vitis Unified Software Platform supports high-level programming in C, C++, OpenCL, and Python, enabling developers to build and seamlessly deploy applications on Xilinx platforms including Alveo cards, FPGA instances in the cloud, and embedded devices. Moreover, Vitis enables the acceleration of large-scale data processing and machine learning applications using familiar high-level frameworks, such as TensorFlow and SPARK. This presentation provides an overview of the Vitis Software platform and the accelerated Vitis Vision Library, which enables customizable functions such as image signal processing, adaptable AI inference, 3D reconstruction and motion analysis.
This is a talk at AI Nextcon Seattle on Feb 12, 2020.
An overview of TensorFlow Lite and various resources for helping you deploy TFLite models to mobile and edge devices. Walk through an example of end to end on-device ML: train a model from scratch, convert to TFLite and deploy it.
Overview of the Linux Kernel, based on "Anatomy of the Linux Kernel" by M. Tim Jones, (IBM Developerworks) http://www.ibm.com/developerworks/linux/library/l-linux-kernel/
Best Practices For Game Development Using Perforce Streams Perforce
To build a future hit, AAA game development teams need to manage a complex environment. Making a game involves a lot of (big) files, many contributors, and millions of changes. The sheer number of branches associated can be overwhelming for any team.
That’s why 19 of the top 20 game development studios choose Helix Core –– version control from Perforce.
Take Sumo Digital. They use Helix Core to manage obstacles, visualize code, and integrate the tools they need. And they use Perforce Streams –– branching and merging in Helix Core –– to guide development and streamline their workflows.
Join Mark Washbrook and Tony Crowther from Sumo Digital, along with Chuck Gehman from Perforce, to learn:
-Key version control challenges for AAA game development.
-What is Perforce Streams?
-How Sumo Digital uses Perforce Streams to integrate with Unreal.
Discover how your team can benefit from using Streams.
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/tensorflow-lite-for-microcontrollers-tflm-recent-developments-a-presentation-from-bdti-and-google/
David Davis, Senior Embedded Software Engineer, and John Withers, Automation and Systems Engineer, both of BDTI, present the “TensorFlow Lite for Microcontrollers (TFLM): Recent Developments” tutorial at the May 2022 Embedded Vision Summit.
TensorFlow Lite Micro (TFLM) is a generic inference framework designed to run TensorFlow models on digital signal processors (DSPs), microcontrollers and other embedded targets with small memory footprints and very low power usage. TFLM aims to be easily portable to various embedded targets from those running an RTOS to bare-metal code. TFLM leverages the model optimization tools from the TensorFlow ecosystem and has additional embedded-specific optimizations to reduce the memory footprint. TFLM also integrates with a number of community contributed optimized hardware-specific kernel implementations.
In this talk, Davis and Withers review collaboration between BDTI and Google over the last year, including porting nearly two dozen operators from TensorFlow Lite to TFLM, creation of a separate Arduino examples repository, improved testing and documentation of both Arduino and Colab training examples and transitioning TFLM’s open-source CI framework to use GitHub Actions.
Bring Intelligence to the Edge with Intel® Movidius™ Neural Compute StickDESMOND YUEN
Motiviation to move intelligence to the edge
Edge compute use cases
Barriers to moving intelligence to the edge
Deep learning algorithms – can they run on an edge device?
Movidius Neural Compute Stick (arch,usage, etc)
OpenNebulaConf 2016 - The Lightweight Approach to Build Cloud CyberSecurity E...OpenNebula Project
In the era of Cloud Service and Internet of Things, information security has already become a transnational issue. In recent years, the large scale cyber attack via the connection of BotNet has become a thorny issue of Global information security. Taiwan is always the main target of international hackers due to the high dense of information devices and computers in campuses are always the favorite of hackers. To help tackling such an issue, the Ezilla, which is considered as a private Cloud toolkit ( integrated with OpenNebula), has been implemented by the CyberSecurity research team in the National Center for High-performance Computing (NCHC), Taiwan. Through the Ezilla which leverages OpenNebula and CyberSecuirty techniques, Cloud users can easily customize and configure a specified Cloud security training environment. It is an extremely lightweight approach helping users to access virtual computing resources. The main feature of this project is simplifying the utilization of Clouds. Our goal is to make Cloud security scientists or users painlessly to run their own CyberSecurity jobs on Cloud platforms, including Cyber Defense Exercise, Malware Knowledge Base, etc.. Based on the proposed CyberSecurity Exercise Platform, we also develop new functions which are private Cloud information security training service, Captur the Flags (CTF) competition service, and virtual networking service for enterprise.
Artificial Intelligence, AI, is changing our lives from the past to the future. It enables machine learning by using a variety of training models to simulate and infer the status or appearance of objects. For example, the inference system with the video analysis model can perform face and vehicle license plate analysis for safety and security purposes.
Today, most of AI technology still rely on the data center to execute the inference, which will increase the risk of real-time application for applications such as traffic monitoring, security CCTV, etc. Therefore, it’s crucial to implement a low-latency. real-time edge computing platform.
Faster deep learning solutions from training to inference - Michele Tameni - ...Codemotion
Intel Deep Learning SDK enables using of optimized open source deep-learning frameworks, including Caffe and TensorFlow through a step-by-step wizard or iPython interactive notebooks. It includes easy and fast installation of all depended libraries and advanced tools for easy data pre-processing and model training, optimization and deployment, providing an end-to-end solution to the problem. In addition, it supports scale-out on multiple computers for training, as well as using compression methods for deployment of the models on various platforms, addressing memory and speed constraints.
D’une infrastructure de virtualisation scripté à un cloud privé OpenNebulaOpenNebula Project
La direction informatique de l’Université de Strasbourg dispose d’un environnement de virtualisation composé de 700 machines virtuelles hébergées sur une centaine d’hyperviseurs. L’administration est faite à l’aide de virt-manager et de scripts python développés en interne. Suite aux nouvelles demandes de ses utilisateurs, la direction informatique a décider de mettre en place une solution de cloud privé. Le choix de l’outil s’est naturellement orienté vers une solution suffisamment flexible, personnalisable et simple pour permettre d’intégrer l’infrastructure existante et de faire face aux besoins de demain.
Talk given by Guillaume Oberlé from Université de Strasbourg (unistra.fr) during Paris Techday 2015
http://opennebula.org/community/techdays/techday-paris-2015/
ONS 2018 LA - Intel Tutorial: Cloud Native to NFV - Alon Bernstein, Cisco & K...Kuralamudhan Ramakrishnan
The first wave of NFV was about taking a network function and running it as-is in a virtual environment. The web giants follow a different approach called Cloud Native. Cloud Native views the cloud as a huge distributed compute platform, applications are broken into micro-services and deployed in a container based environment using DevOps.
Communication Service Providers are looking to adopt Cloud Native, yet the existing Cloud Native principles are not sufficient to meet their business and NFV use case needs. In this session, Intel and Cisco will explore and share experiences addressing challenges, technology gaps and migration path to Cloud Native for NFV.
Join us to alleviate your concerns around data plane performance, control, and DevOps deployment when using micro-services, Containers, and Kubernetes implementations.
DevOps Training in Hyderabad - Visualpath is the Leading and Best Software Online Training institute in Ameerpet. Avail complete job-oriented DevOps Online Training Course by simply enrolling in our institute in Ameerpet. Call on - +91-9989971070.
Visit: https://www.visualpath.in/devops-online-training.html
Deploying Image Classifiers on Intel® Movidius™ Neural Compute StickIntel® Software
In this webinar, Ashwin Vijayakumar will walk through the process of profiling pre-trained neural networks designed for image classification, identify a good balance between accuracy and real-time performance, and write a simple Python* script to deploy these classifiers on the Intel® Movidius™ Neural Compute Stick.
Searching for Embedded Systems,VLSI,Matlab, PLC scada Training Institute in Hyderabad-Get the Best Embedded Systems,VLSI,Matlab, PLC scada Training with Real time Projects from Nanocdac. Register now for new batches Call Us-040 -23754144,+91- 9640648777
Using Open Source technologies to create Enterprise Level Cloud SystemOpenFest team
Using Open Source technologies to create Enterprise Level Cloud System, optimize your costs and offset your carbon footprint on the environment - Венелин Горнишки, Илиян Стоянов
Aiming to The Future with Next
Generation Network Appliance
IEI PUZZLE series is the next generation product of network appliance which includes a broad portfolio of x86-
based and ARM-based network platform built with the latest generation Intel, AMD, Marvell, NXP or Cavium
processors, and Aquantia, Intel, Broadcom, Mellanox network interface controllers. These products are built for
proprietary network appliance and uCPE (Universal Customer Premise Equipment).
AI邊緣運算實作: TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
[1]python程式設計
https://bit.ly/359cz4m
[2]AI機器學習&深度學習
http://bit.ly/2KDZZz4
[3]TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
Tiny ML for spark Fun Edge
https://www.ittraining.com.tw/ittraining/it-elearning/el-ai/ai-tensorflow-lite-for-mcu
TensorFlow Lite for MCU正是專為邊緣裝置設計的TensorFlow模型預測框架，是TensorFlow的精簡版本，讓開發者可以在物聯網與嵌入式裝置中部署微型機器學習模型。 本課程將教授AI模型如何佈署於微控制器，包含模型訓練、模型最佳化以及TensorFlow Lite框架的程式開發等。此外，在實作上以Sparkfun edge board (ARM cortex M4)為例，說明如何以TensorFlow Lite 進行微控制器上面的人工智慧開發專案，包含人臉偵測、關鍵字的字詞偵測、姿態識別、異常偵測等。
https://youtu.be/RHvROP94qZ0
AI邊緣運算實作: TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
[1]python程式設計
https://bit.ly/359cz4m
[2]AI機器學習&深度學習
http://bit.ly/2KDZZz4
[3]TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
5. Enables CNN-based deep learning inference on the
edge
Supports heterogeneous execution across Intel® CPU,
Intel® Integrated Graphics, Intel® Movidius™ Neural
Compute Stick, Intel® Neural Compute Stick 2, and
Intel® Vision Accelerator Design with Intel® Movidius™
VPUs
Speeds time-to-market via an easy-to-use library of
computer vision functions and pre-optimized kernels
Includes optimized calls for computer vision standards
including OpenCV*, OpenCL™, and OpenVX*
OpenVINO™ toolkit
9
6. OpenVINO™ toolkit Workflow
10
The NCSDK only supports the original NCS.
The OpenVINO™ toolkit supports the Intel® NCS 2 and the original NCS.
9. Run OpenVINO Sample Application on Raspberry Pi
安裝 OpenVINO
toolkit 並設定環
境變數
準備好 pre-trained
Model
(xml, bin)
執行應用程式
.xml: Describes the network topology
.bin: Contains the weights and biases binary data
https://docs.openvinotoolkit.org/latest/_docs_install_guides_i
nstalling_openvino_raspbian.html
編譯Sample
Application
13
