For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/a-practical-guide-to-getting-the-dnn-accuracy-you-need-and-the-performance-you-deserve-a-presentation-from-qualcomm/
Felix Baum, Director of Product Management at Qualcomm, presents the “Practical Guide to Getting the DNN Accuracy You Need and the Performance You Deserve” tutorial at the May 2022 Embedded Vision Summit.
Every day, developers struggle to take DNN workloads that were originally developed on workstations and migrate them to run on edge devices. Whether the application is in mobile, compute, IoT, XR or automotive, most AI developers start their algorithm development in the cloud or on a workstation and later migrate to on-device as an afterthought. Qualcomm is helping these developers on multiple fronts—democratizing AI at the edge by supporting frameworks and data types that developers are most familiar with, and at the same time building a set of tools to assist sophisticated developers who are taking extra steps to extract the best performance and power efficiency.
In this session, Baum presents the workflow and steps for effectively migrating DNN workloads to the edge. He discusses quantization issues, explore how the accuracy of models affects performance and power and outline the Qualcomm tools that help developers successfully launch new use cases on mobile and other edge devices.
Qualcomm is an at-scale company. It powered the smartphone revolution and connected billions of people. It pioneered 3G and 4G, and now it is leading the way to 5G and a new era of intelligent, connected devices. Mobile is going to be the largest machine learning platform on the planet. Come learn how Qualcomm is making efficient on-device machine learning possible, how Qualcomm and Facebook worked closely to support machine learning in Facebook applications, and what’s next for Qualcomm and AI.
Qualcomm is an at-scale company. It powered the smartphone revolution and connected billions of people. It pioneered 3G and 4G, and now it is leading the way to 5G and a new era of intelligent, connected devices. Mobile is going to be the largest machine learning platform on the planet. Come learn how Qualcomm is making efficient on-device machine learning possible, how Qualcomm and Facebook worked closely to support machine learning in Facebook applications, and what’s next for Qualcomm and AI.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
With every passing day, organizations are becoming more and more mindful about the performance of their Software Products. However, most of them still on look-out for the basics of Performance Engineering.
According to a recent study by Gartner, fixing performance defects near the end of the development cycle costs 50 to 100 times more than the cost required for fixing it during the early phase of development. Hence, if a product suffers from serious performance issues it can be completely scrapped.
Performance Engineering ensures that your application is performing as per expectations and the software is tested and tuned to meet specified or even the unstated performance requirements.
We present you with a webcast on Performance Engineering Basics that would walk you through the elements and process of performance engineering, and also offers a methodical process for the same.
It also offers details on a load testing tool, and describes how best to utilize it.
Visit http: http://www.impetus.com/featured_webcast?eventid=10 to listen to the entire webcast (20 minutes).
OR
To post any queries on Performance Engineering, write to us at isales@impetus.com
For case studies and articles on performance engineering please visit: http://www.impetus.com/plabs/casestudies?case_study=&pLabsClustering.pdf=
Modernizing Testing as Apps Re-ArchitectDevOps.com
Applications are moving to cloud and containers to boost reliability and speed delivery to production. However, if we use the same old approaches to testing, we'll fail to achieve the benefits of cloud. But what do we really need to change? We know we need to automate tests, but how do we keep our automation assets from becoming obsolete? Automatically provisioning test environments seems close, but some parts of our applications are hard to move to cloud.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/benchmarking-vs-benchmarketing-why-should-you-care-a-presentation-from-qualcomm/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Felix Baum, Director of Product Management at Qualcomm, presents the “Benchmarking vs. Benchmarketing: Why Should You Care?” tutorial at the September 2020 Embedded Vision Summit.
Qualcomm is determined to provide best in class AI hardware solutions, enabling companies to leverage AI acceleration in their products. But how can developers know what is the best hardware for their models? Comparing AI hardware is not as simple as it might seem; there are many caveats that need to be considered, such as INT8 and floating-point benefits, how commercial benchmarks are structured and what is the hardware optimized for.
Qualcomm chips score at the top of commercial benchmarks. Nonetheless, the company is devoted to enlightening its partners and developers about what is the best solution for their specific needs. In this talk you will learn about some of the most common ways of comparing AI hardware and what you need to consider in order to make an accurate assessment.
This session will provide a high-level overview of all of the tools and SDKs offered to Android developers via the Qualcomm Developer Network. Covered topics include Qualcomm® Adreno™ graphics profiler, Qualcomm® Snapdragon™ LLVM compiler, Trepn™ power profiler, Qualcomm® MARE parallelization library, Adreno SDK, AllJoyn™ software framework, Qualcomm®Vuforia™ SDK and more.
Watch this presentation on YouTube: https://www.youtube.com/watch?v=ItrzdkBmRJQ
Learn more about Android Tools for Snapdragon processors at Qualcomm Developer Network: https://developer.qualcomm.com/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/accelerating-newer-ml-models-using-the-qualcomm-ai-stack-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director and Head of AI/ML Product Management at Qualcomm Technologies, presents the “Accelerating Newer ML Models Using the Qualcomm AI Stack” tutorial at the May 2023 Embedded Vision Summit.
The Qualcomm AI Stack revolutionizes how Qualcomm thinks about AI software and provides the ultimate tool and user interface to enable ecosystem partners to create faster and smarter AI applications for all embedded form factors. Focusing on real user experience challenges centered around model deployment, Sakumar explains how the Snapdragon developer community leverages data types, quantization and neural architecture search—among others—to optimize complex AI architectures for emerging use cases.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/deploying-large-models-on-the-edge-success-stories-and-challenges-a-presentation-from-qualcomm/
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.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/scaling-vision-based-edge-ai-solutions-from-prototype-to-global-deployment-a-presentation-from-network-optix/
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.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.
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MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
With every passing day, organizations are becoming more and more mindful about the performance of their Software Products. However, most of them still on look-out for the basics of Performance Engineering.
According to a recent study by Gartner, fixing performance defects near the end of the development cycle costs 50 to 100 times more than the cost required for fixing it during the early phase of development. Hence, if a product suffers from serious performance issues it can be completely scrapped.
Performance Engineering ensures that your application is performing as per expectations and the software is tested and tuned to meet specified or even the unstated performance requirements.
We present you with a webcast on Performance Engineering Basics that would walk you through the elements and process of performance engineering, and also offers a methodical process for the same.
It also offers details on a load testing tool, and describes how best to utilize it.
Visit http: http://www.impetus.com/featured_webcast?eventid=10 to listen to the entire webcast (20 minutes).
OR
To post any queries on Performance Engineering, write to us at isales@impetus.com
For case studies and articles on performance engineering please visit: http://www.impetus.com/plabs/casestudies?case_study=&pLabsClustering.pdf=
Modernizing Testing as Apps Re-ArchitectDevOps.com
Applications are moving to cloud and containers to boost reliability and speed delivery to production. However, if we use the same old approaches to testing, we'll fail to achieve the benefits of cloud. But what do we really need to change? We know we need to automate tests, but how do we keep our automation assets from becoming obsolete? Automatically provisioning test environments seems close, but some parts of our applications are hard to move to cloud.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/11/benchmarking-vs-benchmarketing-why-should-you-care-a-presentation-from-qualcomm/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Felix Baum, Director of Product Management at Qualcomm, presents the “Benchmarking vs. Benchmarketing: Why Should You Care?” tutorial at the September 2020 Embedded Vision Summit.
Qualcomm is determined to provide best in class AI hardware solutions, enabling companies to leverage AI acceleration in their products. But how can developers know what is the best hardware for their models? Comparing AI hardware is not as simple as it might seem; there are many caveats that need to be considered, such as INT8 and floating-point benefits, how commercial benchmarks are structured and what is the hardware optimized for.
Qualcomm chips score at the top of commercial benchmarks. Nonetheless, the company is devoted to enlightening its partners and developers about what is the best solution for their specific needs. In this talk you will learn about some of the most common ways of comparing AI hardware and what you need to consider in order to make an accurate assessment.
This session will provide a high-level overview of all of the tools and SDKs offered to Android developers via the Qualcomm Developer Network. Covered topics include Qualcomm® Adreno™ graphics profiler, Qualcomm® Snapdragon™ LLVM compiler, Trepn™ power profiler, Qualcomm® MARE parallelization library, Adreno SDK, AllJoyn™ software framework, Qualcomm®Vuforia™ SDK and more.
Watch this presentation on YouTube: https://www.youtube.com/watch?v=ItrzdkBmRJQ
Learn more about Android Tools for Snapdragon processors at Qualcomm Developer Network: https://developer.qualcomm.com/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/06/accelerating-newer-ml-models-using-the-qualcomm-ai-stack-a-presentation-from-qualcomm/
Vinesh Sukumar, Senior Director and Head of AI/ML Product Management at Qualcomm Technologies, presents the “Accelerating Newer ML Models Using the Qualcomm AI Stack” tutorial at the May 2023 Embedded Vision Summit.
The Qualcomm AI Stack revolutionizes how Qualcomm thinks about AI software and provides the ultimate tool and user interface to enable ecosystem partners to create faster and smarter AI applications for all embedded form factors. Focusing on real user experience challenges centered around model deployment, Sakumar explains how the Snapdragon developer community leverages data types, quantization and neural architecture search—among others—to optimize complex AI architectures for emerging use cases.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/deploying-large-models-on-the-edge-success-stories-and-challenges-a-presentation-from-qualcomm/
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.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/scaling-vision-based-edge-ai-solutions-from-prototype-to-global-deployment-a-presentation-from-network-optix/
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.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/whats-next-in-on-device-generative-ai-a-presentation-from-qualcomm/
Jilei Hou, Vice President of Engineering and Head of AI Research at Qualcomm Technologies, presents the “What’s Next in On-device Generative AI” tutorial at the May 2024 Embedded Vision Summit.
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.
In this talk, Hou shares Qualcomm’s vision of the compelling opportunities enabled by efficient generative AI at the edge. He also identifies the key challenges that the industry must overcome to realize the massive potential of these technologies. And he highlights research and product development work that Qualcomm is doing to lead the way via an end-to-end system approach—including techniques for efficient on-device execution of LLMs, LVMs and LMMs, methods for orchestration of large models at the edge and approaches for adaptation and personalization.
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/
Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/introduction-to-computer-vision-with-cnns-a-presentation-from-mohammad-haghighat/
Independent consultant Mohammad Haghighat presents the “Introduction to Computer Vision with Convolutional Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
This presentation covers the basics of computer vision using convolutional neural networks. Haghighat begins by introducing some important conventional computer vision techniques and then transition to explaining the basics of machine learning and convolutional neural networks (CNNs) and showing how CNNs are used in visual perception.
Haghighat illustrates the building blocks and computational elements of neural networks through examples. This session provides an overview of how modern computer vision algorithms are designed, trained and used in real-world applications.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/selecting-tools-for-developing-monitoring-and-maintaining-ml-models-a-presentation-from-yummly/
Parshad Patel, Data Scientist at Yummly, presents the “Selecting Tools for Developing, Monitoring and Maintaining ML Models” tutorial at the May 2023 Embedded Vision Summit.
With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers.
Fortunately, most proprietary, fee-based tools have an open-source component that can be integrated into a home-grown solution at low cost. This can be a good starting point, enabling teams to get started with modern tools without making big investments and leaving the door open to evolve tool selection over time. In this talk, Patel presents a step-by-step process for creating an MLOps tool set that enables you to deliver maximum value as a data scientist. He shares how Yummly built pipelines for model development and put them into production using open-source projects.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/building-accelerated-gstreamer-applications-for-video-and-audio-ai-a-presentation-from-wave-spectrum/
Abdo Babukr, Accelerated Computing Consultant at Wave Spectrum, presents the “Building Accelerated GStreamer Applications for Video and Audio AI,” tutorial at the May 2023 Embedded Vision Summit.
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.
In this talk, Babukr introduces GStreamer and shows how to use it to build computer vision and audio perception applications. He also shows how to create efficient, high-performance GStreamer applications that utilize specialized hardware features.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
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.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/introduction-to-modern-lidar-for-machine-perception-a-presentation-from-the-university-of-ottawa/
Robert Laganière, Professor at the University of Ottawa and CEO of Sensor Cortek, presents the “Introduction to Modern LiDAR for Machine Perception” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Laganière provides an introduction to light detection and ranging (LiDAR) technology. He explains how LiDAR sensors work and their main advantages and disadvantages. He also introduces different approaches to LiDAR, including scanning and flash LiDAR.
Laganière explores the types of data produced by LiDAR sensors and explains how this data can be processed using deep neural networks. He also examines the synergy between LiDAR and cameras, and the concept of pseudo-LiDAR for detection.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/vision-language-representations-for-robotics-a-presentation-from-the-university-of-pennsylvania/
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.
Jayaraman discusses recent advances in unsupervised representation learning and explains how these approaches can be used to build visual representations that are appropriate for a controller that decides how the robot should act. In particular, he presents insights from his research group’s recent work on how to represent the constituent objects and entities in a visual scene, and how to combine vision and language in a way that permits effectively translating language-based task descriptions into images depicting the robot’s goals.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/adas-and-av-sensors-whats-winning-and-why-a-presentation-from-techinsights/
Ian Riches, Vice President of the Global Automotive Practice at TechInsights, presents the “ADAS and AV Sensors: What’s Winning and Why?” tutorial at the May 2023 Embedded Vision Summit.
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, Riches explores likely future demand for automotive radars, cameras and LiDARs.
Riches examines which vehicle features will drive demand out to 2030, how vehicle architecture change is impacting the market and what sorts of compute platforms these sensors will be connected to. Finally, he shares his firm’s vision of what the landscape could look like far beyond 2030, considering scenarios out to 2050 for automated driving and the resulting sensor demand.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/computer-vision-in-sports-scalable-solutions-for-downmarkets-a-presentation-from-sportlogiq/
Mehrsan Javan, Co-founder and CTO of Sportlogiq, presents the “Computer Vision in Sports: Scalable Solutions for Downmarket Leagues” tutorial at the May 2023 Embedded Vision Summit.
Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports.
In this talk, Javan explains how his company has developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. He highlights key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. He discusses the approaches Sportlogiq tried and how it ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/detecting-data-drift-in-image-classification-neural-networks-a-presentation-from-southern-illinois-university/
Spyros Tragoudas, Professor and School Director at Southern Illinois University Carbondale, presents the “Detecting Data Drift in Image Classification Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, Tragoudas presents a novel scheme for diagnosing data drift in the input streams of image classification neural networks. His proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model.
The method is applicable to any drift type in images such as noise and weather effects, among others. Tragoudas shares experimental results on various data sets, drift types and neural network models to show that his proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/ai-start-ups-the-perils-of-fishing-for-whales-war-stories-from-the-entrepreneurial-front-lines-a-presentation-from-seechange-technologies/
Tim Hartley, Vice President of Product for SeeChange Technologies, presents the “AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepreneurial Front Lines)” tutorial at the May 2023 Embedded Vision Summit.
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
In this talk, Hartley lays out some of the pitfalls that await those building the next big thing. Using real examples, he shares some of the dos and don’ts, particularly when dealing with that big potential first customer. Hartley discusses the importance of end-to-end design, ensuring your product solves real-world problems. He explores how far the big companies will tell you to jump—and then jump again—for free. And, most importantly, how to build long-term partnerships with major corporations without relying on over-promising sales pitches.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/a-computer-vision-system-for-autonomous-satellite-maneuvering-a-presentation-from-scout-space/
Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
Computer vision systems for mobile autonomous machines experience a wide variety of real-world conditions and inputs that can be challenging to capture accurately in training datasets. Few autonomous systems experience more challenging conditions than those in orbit. In this talk, Harris describes how SCOUT Space has designed and trained satellite vision systems using dynamic and physically informed synthetic image datasets.
Harris describes how his company generates synthetic data for this challenging environment and how it leverages new real-world data to improve our datasets. In particular, he explains how these synthetic datasets account for and can replicate real sources of noise and error in the orbital environment, and how his company supplements them with in-space data from the first SCOUT-Vision system, which has been in orbit since 2021.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/bias-in-computer-vision-its-bigger-than-facial-recognition-a-presentation-from-santa-clara-university/
Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit.
As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias.
This talk explores how bias in computer vision often poses an ethical challenge, regardless of the stakes involved. Kennedy discusses the limitations of technical solutions aimed at mitigating bias, and why “bias-free” AI may not be achievable. Instead, she focuses on the importance of adopting a “bias-aware” approach to responsible AI design and explores strategies that can be employed to achieve this.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/sensor-fusion-techniques-for-accurate-perception-of-objects-in-the-environment-a-presentation-from-sanborn-map-company/
Baharak Soltanian, Vice President of Research and Development for the Sanborn Map Company, presents the “Sensor Fusion Techniques for Accurate Perception of Objects in the Environment” tutorial at the May 2023 Embedded Vision Summit.
Increasingly, perceptual AI is being used to enable devices and systems to obtain accurate estimates of object locations, speeds and trajectories. In demanding applications, this is often best done using a heterogeneous combination of sensors (e.g., vision, radar, LiDAR). In this talk, Soltanian introduces techniques for combining data from multiple sensors to obtain accurate information about objects in the environment.
Soltanian briefly introduces the roles played by Kalman filters, particle filters, Bayesian networks and neural networks in this type of fusion. She then examines alternative fusion architectures, such as centralized and decentralized approaches, to better understand the trade-offs associated with different approaches to sensor fusion as used to enhance the ability of machines to understand their environment.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/updating-the-edge-ml-development-process-a-presentation-from-samsara/
Jim Steele, Vice President of Embedded Software at Samsara, presents the “Updating the Edge ML Development Process” tutorial at the May 2023 Embedded Vision Summit.
Samsara (NYSE:IOT) is focused on digitizing the world of operations. The company helps customers across many industries—including food and beverage, utilities and energy, field services and government—get information about their physical operations into the cloud, so they can operate more safely, efficiently and sustainably. Samsara’s sensors collect billions of data points per day and on-device processing is instrumental to its success. The company is constantly developing, improving and deploying ML models at the edge.
Samsara has found that the traditional development process—where ML scientists create models and hand them off to firmware engineers for embedded implementation—is slow and often produces difficult-to-resolve differences between the original model and the embedded implementation. In this talk, Steele presents an alternative development process that his company has adopted with good results. In this process, firmware engineers develop a general framework that ML scientists use to design, develop and deploy their models. This enables quick iterations and fewer confounding bugs.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/combating-bias-in-production-computer-vision-systems-a-presentation-from-red-cell-partners/
Alex Thaman, Chief Architect at Red Cell Partners, presents the “Combating Bias in Production Computer Vision Systems” tutorial at the May 2023 Embedded Vision Summit.
Bias is a critical challenge in predictive and generative AI that involves images of humans. People have a variety of body shapes, skin tones and other features that can be challenging to represent completely in training data. Without attention to bias risks, ML systems have the potential to treat people unfairly, and even to make humans more likely to do so.
In this talk, Thaman examines the ways in which bias can arise in visual AI systems. He shares techniques for detecting bias and strategies for minimizing it in production AI systems.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
“A Practical Guide to Getting the DNN Accuracy You Need and the Performance You Deserve,” a Presentation from Qualcomm
1. A Practical Guide to
Getting the DNN
Accuracy You Need
and the Performance
You Deserve
Felix Baum
Director, Product Management
Qualcomm Technologies, Inc.
Snapdragon is a product of Qualcomm
Technologies, Inc. and/or its subsidiaries.
2. Qualcomm Technologies AI software stack
Supporting every AI software layer from applications to the metal
2
2022 Qualcomm Technologies, Inc.
Runtime
Qualcomm®
Neural Processing SDK
Android Neural
Networks API
SDKs
ResNet DeepLab
MobileNet
SSD
Mobile
BERT
VDSR
Models
Qualcomm® AI
Engine direct
NNAPI
Frameworks
Applications
AIMET
TVM
Tools +
Compilers
Qualcomm Neural Processing SDK and Qualcomm AI Engine Direct
are products of Qualcomm Technologies, Inc. and/or its subsidiaries
3. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
AI software workflow today
2022 Qualcomm Technologies, Inc. 3
Train,
Finetune
model
Data
Scientist
ML Training
Engineer
Environment Training
Legend Customer chosen sw.
Customer ML Workflow Target Usage
SNPE & Qualcomm AI
Engine Direct Workflow
4. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Does the
model
compile?
AI software workflow today
2022 Qualcomm Technologies, Inc. 4
Train,
Finetune
model
Add custom
layers and/or
fix errors
Qualcomm
AI Engine Direct
& Qualcomm
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
Formats
supported
Data
Scientist
ML Training
Engineer
Environment Training Compilation
Legend Customer chosen sw.
Customer ML Workflow Target Usage
SNPE & Qualcomm AI
Engine Direct Workflow
5. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
AI software workflow today
2022 Qualcomm Technologies, Inc. 5
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm
AI Engine Direct
& Qualcomm
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Data
Scientist
ML Training
Engineer
Environment Training Compilation Accuracy analysis
Legend Customer chosen sw.
Customer ML Workflow Target Usage
SNPE & Qualcomm AI
Engine Direct Workflow
6. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
AI software workflow today
2022 Qualcomm Technologies, Inc. 6
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
Environment Training Compilation Accuracy analysis Optimizations
Qualcomm
AI Engine Direct
& Qualcomm
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow Target Usage
SNPE & Qualcomm AI
Engine Direct Workflow
Hexagon
Profiler
& Trace
Analyzer
7. AI software workflow today
2022 Qualcomm Technologies, Inc. 7
Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow Target Usage
SNPE & Qualcomm AI
Engine Direct Workflow
8. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
AI software workflow today
2022 Qualcomm Technologies, Inc. 8
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow
SNPE & Qualcomm AI
Engine Direct Workflow
Target Usage
Quantization Tuner
Automated
quantization using all
quantization options
and determine best
options for best
accuracy for model
Ranks accuracy using
different verifiers for all
quantization options
matrix
9. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
AI software workflow today
2022 Qualcomm Technologies, Inc. 9
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow
SNPE & Qualcomm AI
Engine Direct Workflow
Target Usage
Quantization Tuner
Automated
quantization using all
quantization options
and determine best
options for best
accuracy for model
Ranks accuracy using
different verifiers for all
quantization options
matrix
Performance
Analyzer
A new QNN HTP perf
profile exposes
bottlenecks in
network execution by
showing expanded
analysis of
contribution of ops to
executive timelines
10. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
AI software workflow today
2022 Qualcomm Technologies, Inc. 10
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow
SNPE & Qualcomm AI
Engine Direct Workflow
Target Usage
Quantization Tuner
Automated
quantization using all
quantization options
and determine best
options for best
accuracy for model
Ranks accuracy using
different verifiers for all
quantization options
matrix
Performance
Analyzer
A new QNN HTP perf
profile exposes
bottlenecks in
network execution by
showing expanded
analysis of
contribution of ops to
executive timelines
Hexagon Instrumentation Profiler
Provide insights into the system by
collecting cycle counts, PMU counters
& other metrics
11. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
AI software workflow today
2022 Qualcomm Technologies, Inc. 11
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow
SNPE & Qualcomm AI
Engine Direct Workflow
Target Usage
Quantization Tuner
Automated
quantization using all
quantization options
and determine best
options for best
accuracy for model
Ranks accuracy using
different verifiers for all
quantization options
matrix
Performance
Analyzer
A new QNN HTP perf
profile exposes
bottlenecks in
network execution by
showing expanded
analysis of
contribution of ops to
executive timelines
Hexagon Instrumentation Profiler
Provide insights into the system by
collecting cycle counts, PMU counters
& other metrics
Hexagon VS Code
Improved IDE for
debugging, profiling,
and trace analysis
12. Choose env.,
config.,
model and
framework
Does the model
meet
performance
metrics
Model
Compilation/
Runner
Accuracy
Evaluation
Does the
model
compile?
Is the
model’s
accuracy
acceptable?
Is the model’s
output &
latency
acceptable?
Integrate
model into App
or pipeline
Deploy
App
AI software workflow today
2022 Qualcomm Technologies, Inc. 12
Train,
Finetune
model
Add custom
layers and/or
fix errors
Debug and
fix errors
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural
Processing
SDK Converter
and Quantizer Custom Ops
LLVM (C/C++)
TVM (Python)
AI model
Efficiency
Toolkit
Formats
supported
Profile
model Perf
Did that
work?
Use advanced
optimization
techniques
Hexagon
Profiler
& Trace
Analyzer
Data
Scientist
ML Training
Engineer
ML Inference
Engineer
App
Developer
DevOps
Engineer
Environment Training Compilation Accuracy analysis Optimizations Integration Deployment
Qualcomm®
AI Engine Direct
& Qualcomm®
Neural Processing
SDK Profilers
Legend Customer chosen sw.
Customer ML Workflow
SNPE & Qualcomm AI
Engine Direct Workflow
Target Usage
Quantization Tuner
Automated
quantization using all
quantization options
and determine best
options for best
accuracy for model
Ranks accuracy using
different verifiers for all
quantization options
matrix
Performance
Analyzer
A new QNN HTP perf
profile exposes
bottlenecks in
network execution by
showing expanded
analysis of
contribution of ops to
executive timelines
Hexagon Instrumentation Profiler
Provide insights into the system by
collecting cycle counts, PMU counters
& other metrics
Hexagon VS Code
Improved IDE for
debugging, profiling,
and trace analysis
QNN HTP
Simulator
QeMU based
simulation
environment for bit
accurate validation
of accuracy of the
execution
13. Not all applications are built the same way, your software
stack will determine how well your application will perform
In order to achieve your application full capacity, you need a
software stack that is tailored to specifically to what you are
looking to accomplish
Different models require specific tools that only customizable
stacks will offer
Take away
13
2022 Qualcomm Technologies, Inc.
14. Resources
14
2022 Qualcomm Technologies, Inc.
2022 Embedded Vision Summit
“Powering the Intelligent Connected Edge and the Future of
On-Device AI”
Ziad Asghar May 18 9:30 - 10:00 AM PT
“Seamless Deployment of Multimedia and Machine Learning
Applications at the Edge”
Megha Daga May 17 2:40 - 3:10 PM PT
"Autonomous Driving AI Workloads: Technology Trends and
Optimization Strategies“
Ahmed Sadek May 17 2:05 – 2:35 PM PT
“Tools for Creating Next-Gen Computer Vision Apps on
Snapdragon”
Judd Heape May 18 10:50 - 11:20 AM PT
“The Future of AI is Here Today: Deep Dive into Qualcomm’s
On-Device AI Offerings”
Vinesh Sukumar May 18 12:00 - 12:30 PM PT
Qualcomm AI page:
https://www.qualcomm.com/invention/artificial-intelligence
Qualcomm AI Research:
https://www.qualcomm.com/invention/artificial-intelligence/ai-
research?cmpid=fofyus193556&gclid=CjwKCAjw19z6BRAYEiwAmo64LfQ
jU8vqH8TxqKTM2PZQp8JibXrjev85wLfKFknJnS_b494yZ7e_WhoCPQkQAv
D_BwE
Qualcomm Platform Solution Ecosystem:
https://www.qualcomm.com/support/qan/platform-solutions-ecosystem
GitHub AI Model Efficiency Toolkit (AIMET):
https://github.com/quic/aimet
Qualcomm Mobile AI page:
https://www.qualcomm.com/products/smartphones/mobile-ai
Qualcomm Mobile AI blog:
https://www.qualcomm.com/news/onq/2020/12/02/exploring-ai-
capabilities-qualcomm-snapdragon-888-mobile-platform
Felix Baum, Director, Product Management
fbaum@qti.qualcomm.com