“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility,” a Presentation from Edge Impulse
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/how-do-we-enable-edge-ml-everywhere-data-reliability-and-silicon-flexibility-a-presentation-from-edge-impulse/ Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility” tutorial at the May 2022 Embedded Vision Summit. In this talk, Shelby reveals insights from the company’s recent global edge ML developer survey, which identified key barriers to machine learning adoption, and shares the company’s vision for how the industry can overcome these obstacles. Unsurprisingly, the first critical obstacle identified by the survey is data. But the issue isn’t simply a lack of massive datasets, as is often assumed. On the contrary, the biggest opportunities in ML will be enabled by highly custom, industry-specific and even user-specific data. We need to master data lifecycle and active learning techniques that enable developers to move quickly from “zero to dataset.” The real and perceived inability of today’s ML algorithms to reach the ultra-high accuracy needed in industrial systems is another key barrier. New techniques for explainable ML, better testing, sensor fusion and model fusion will increasingly allow developers to achieve industrial-grade reliability. Finally, in order to accelerate ML adoption in embedded products, we must recognize that most developers can’t immediately upgrade their systems to use the latest chips — a problem that is compounded by today’s chip shortages. To enable ML everywhere, we have to find ways to deploy ML on today’s silicon, while ensuring a smooth transition to new devices with AI acceleration in the future.
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Jayson Bethurem, Vice President of Marketing and Business Development at Flex Logix, presents the “Maximize Your AI Compatibility with Flexible Pre- and Post-processing” tutorial at the May 2024 Embedded Vision Summit.
At a time when IC fabrication costs are skyrocketing and applications have increased in complexity, it is important to minimize design risks and maximize flexibility. In this presentation, you’ll learn how embedding FPGA technology can solve these problems—expanding your market access by enabling more external interfaces, accelerating your compute envelope and increasing data security.
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Download our special report, IoT Tech for the Manager: http://bit.ly/report1-slideshare
Hey IT, Meet OT as presented at the IoT Inc Business' fifteenth Meetup. See: http://www.iot-inc.com/hey-it-meet-ot-meetup/
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Presentation Abstract
Software has been the domain of information technology, but it is quickly becoming key to operations technology as well. Operating smart, networked machines from wind turbines to jet engines requires an intricate understanding of both the machines and the data and information that flows through them. The combination of these two disciplines is bringing new efficiencies and capabilities that do more—faster and cheaper. The key is leveraging connectivity, data, and mobility to optimize efficiency and deliver new services to customers. Join Hima Mukkamala of GE Digital to hear how software technology can help companies bridge the divide between IT and OT and how IT can help industrial companies build, deploy, and manage Industrial Internet applications that bring game-changing efficiencies to businesses.
Introduction to architecture exploration
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Continuous verification from planning to development addresses the multi-discipline needs of hardware, software, and networks. This unique approach accelerates the design phase, defines the test efforts, and finds defects during specification. Architecture modeling is required to meet timing deadlines, generate the lowest power consumption, and attain the highest Quality-of-Service. optimize the electronic design system and designing of custom components.
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Shelby explains the new paradigm of data-driven engineering with ML, showing how developers are using data instead of code to drive algorithm innovation. To support widespread deployment, ML workloads need to run on embedded computing targets from MCUs to GPUs, with MLOps processes to support efficient development and deployment. Industrial, logistics and health markets are particularly ripe to deploy this data-driven approach, and Shelby highlights several exciting case studies.Neo4j: The path to success with Graph Database and Graph Data Science
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Cisco Connect Ottawa 2018 dna assurance shortest path to network innocence
Cisco Connect events bring together technical education, networking opportunities, and expert insight for communities worldwide. They offer insights from Cisco leadership on strategic direction and partnerships, as well as educational content on current and future technologies. Unlike other events, Cisco Connect is specifically for Cisco customers, partners, and those with Cisco certifications.
Trends in Systems and How to Get Efficient Performance
In this video from Switzerland HPC Conference, Martin Hilgeman from Dell presents: HPC Workload Efficiency and the Challenges for System Builders.
"With all the advances in massively parallel and multi-core computing with CPUs and accelerators it is often overlooked whether the computational work is being done in an efficient manner. This efficiency is largely being determined at the application level and therefore puts the responsibility of sustaining a certain performance trajectory into the hands of the user. It is observed that the adoption rate of new hardware capabilities is decreasing and lead to a feeling of diminishing returns. This presentation shows the well-known laws of parallel performance from the perspective of a system builder. It also covers through the use of real case studies, examples of how to program for energy efficient parallel application performance."
Watch the video: http://wp.me/p3RLHQ-gIS
Learn more: http://dell.com
and
http://www.hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
“Deploy Your Embedded Vision Solution on Any Processor Using Edge Impulse,” A...
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Vision-based product developers have a vast array of processors to choose from. Unfortunately, each processor has its own unique tool chain. This makes it difficult for developers to evaluate processor options and target different processors for different product needs. Fortunately, there’s Edge Impulse Studio—a universal development solution that is compatible with any vision-based processor. For example, when using Arm processors, developers can target Arm Cortex-M MCUs, the latest Cortex-M55, the Ethos U55 microNPU, and Cortex-A CPUs—along with the GPUs that are often used with them.
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Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
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Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
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Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
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The generative AI era has begun! Large multimodal models are bringing the power of language understanding to machine perception, and transformer models are expanding to allow machines to understand using multiple types of sensors. This new wave of approaches is poised to revolutionize user experiences, disrupt industries and enable powerful new capabilities. For generative AI to reach its full potential, however, we must deploy it on edge devices, providing improved latency, pervasive interaction and enhanced privacy.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/learning-compact-dnn-models-for-embedded-vision-a-presentation-from-the-university-of-maryland-at-college-park/
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GStreamer is a popular open-source framework for creating streaming media applications. Developers often use GStreamer to streamline the development of computer vision and audio perception applications. Since perceptual algorithms are often quite demanding in terms of processing performance, in many cases developers need to find ways to accelerate key GStreamer building blocks, taking advantage of specialized features of their target processor or co-processor.
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Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
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Dinesh Jayaraman, Assistant Professor at the University of Pennsylvania, presents the “Vision-language Representations for Robotics” tutorial at the May 2023 Embedded Vision Summit.
In what format can an AI system best present what it “sees” in a visual scene to help robots accomplish tasks? This question has been a long-standing challenge for computer scientists and robotics engineers. In this presentation, Jayaraman provides insights into cutting-edge techniques being used to help robots better understand their surroundings, learn new skills with minimal guidance and become more capable of performing complex tasks.
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- 17. Hardware is sexy again! 17 MCU + FPU CPU GPU MCU + NPU Low-end MCU Sensor Audio Image Video Wakeup NPU NDP100 NDP200 Akida M55/U55 Katana RA MCU PSoC 6 © 2022 Edge Impulse xG24
- 18. Hardware is sexy again! 18 MCU + FPU CPU + NPU NPU + GPU MCU + NPU Low-end MCU Sensor Audio Image Video TD4VA Wakeup NPU NDP200 RZ MPU NDP100 RA MCU PSoC 6 © 2022 Edge Impulse Akida M55/U55 Katana xG24
- 19. • The power of hardware profiling • Digital twin of ML on hardware • We are combining hardware profiling with hyperparameter search – EON Tuner • Hardware-aware AutoML across data, pre- processing and ML blocks Hardware profiling & tuning 19 © 2022 Edge Impulse
- 20. FOMO: Faster objects, more objects ● Object detection on constrained compute ● 20x performance improvement ● Scales down to 100k RAM to enable MCUs ● Better at detecting smaller and more numerous objects ● Capable of segmentation and counting objects Cortex-M4 Cortex-M7 Cortex-A Nvidia FOMO 2 fps 15-30 fps 60+ fps 150+ fps SSD NA NA 3 fps 20 fps © 2022 Edge Impulse 20
- 21. Resources Increasing Manufacturing Efficiency https://casestudies.edgeimpulse.com/industrial-iot-with-advantech Constrained object detection: FOMO Blog docs.edgeimpulse.com/docs/tutorials/fomo-object-detection-for- constrained-devices Request a demo edgeimpulse.com/schedule-a-demo 21 © 2022 Edge Impulse
- 22. Don’t miss these talks! FOMO: Real-time Object Detection on Microcontrollers Jan Jongboom, Co-founder and CTO, Edge Impulse • Date: Wednesday, May 18 • Start Time: 10:50 am Deep Dive: Develop and Deploy Advanced Edge Computer Vision—Fast! Jenny Plunkett and Shawn Hymel, Snr. DevRel engineers • Date: Thursday, May 19 • Time: 9 am – 12:00 pm 22 © 2022 Edge Impulse