May 2018 Embedded Vision Summit Vision Tank Start-up Competition Finalist Presentation: Boulder AI
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For the full video of this and the other Vision Tank presentations, please visit: https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-vision-tank For more information about embedded vision, please visit: http://www.embedded-vision.com Dan Connors, CTO of Boulder AI, delivers his company's Vision Tank start-up competition finalist presentation at the May 2018 Embedded Vision Summit.
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In this talk, you’ll hear about the new Hailo-15 AI vision processor family, specifically designed to empower smart cameras with unprecedented AI capabilities. Iarovici explains how the Hailo-15 family of processors—with performance ranging from 7 to 20 tera operations per second—allows cameras to carry out significantly more video analytics at once, running several AI tasks in parallel.
Iarovici shows how Hailo-15 processors make it possible to perform faster detection at high resolution, enabling identification of smaller and more distant objects with higher accuracy and fewer false alarms. He also explores how the elevated AI performance of these processors can be utilized for video enhancement, such as improved low-light video quality, video stabilization and high dynamic range imaging. And he shows how Hailo is pioneering the use of vision-based transformers for real-time object detection in cameras.
Reinforcement Learning - Mindbridge.ai
The document describes an experiment conducted by students to send an AI into space on a memory chip to test how cosmic radiation affects it. The students:
1. Designed an AI using reinforcement learning to land a simulated lunar lander.
2. Selected the best performing AI and loaded it onto memory chips.
3. Assembled the chips into a payload and attached sensors to measure radiation exposure.
4. The payload was sent into space to test if radiation would damage the AI and affect its ability to land the simulator.
Facet technology 4DFlash(tm) vision sensor for vehicles
This document discusses Facet Technology, a provider of machine vision processing hardware, software, and intellectual property. It describes Facet's 4DFlash technology, a vision sensor that integrates LiDAR, camera, and radar data from a single sensor. Key advantages of 4DFlash over other vision sensors include higher resolution (5.2 MP), longer range (200m), superior performance in adverse weather, and lower overall system cost. The document promotes potential partnerships for Facet to license its sensor technology and intellectual property to automotive companies.
AI - Last Year Progress (2018-2019)
The most significant (not purely scientific) results in AI in the last year (2018-2019).
Disclaimer: may be very subjective :)
Slides to the set of lectures given in Feb-Apr 2019.
This one was conducted in Atlas Biomed Group, 2019-04-26
Ai lastyearprogress-atlas-2019-04-26-190428120255
This document provides a summary of recent progress and developments in artificial intelligence from 2018 to early 2019. It discusses advances in photorealistic content generation, natural language processing, speech and sound, games, medicine and biology, large-scale real-life applications, cloud AI, and edge AI. Some of the key developments mentioned include Deepfakes, BERT, GPT-2, AlphaGo Zero, AlphaZero, AlphaStar, DeepMind's AlphaFold, AI assistants, autonomous delivery robots, AI-optimized mobile chips, and the increasing use of cloud-based AI services.
MIPI DevCon Seoul 2018: Powering AI and Automotive Applications with the MIPI...
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Securing future connected vehicles and infrastructure
Slides from a keynote I gave at AZ Infragard. Since this was a keynote, I tried to dazzle the audience by talking more about technology and portraying security only as part of the underlying architecture of cognitive autonomous systems.
VR AR 產業趨勢概況與國際大廠策略分析
This document provides an overview of virtual reality (VR) and augmented reality (AR) industries and trends. It defines key terms like VR, AR, 360 video. It describes the state of hardware devices, software platforms and content for VR/AR. It analyzes the market size and ecosystem including players in hardware, software, content and applications. It outlines many application areas of VR/AR like gaming, entertainment, healthcare, engineering, education and cultural preservation. The document aims to help understand the VR/AR industry landscape and development trends.
Dell AI and HPC University Roadshow
The document discusses Dell Technologies' artificial intelligence (AI) and data analytics solutions portfolio. It provides an overview of Dell's solutions for AI/machine learning, IoT/streaming data, augmented analytics/data warehousing, data lakes, and high-performance computing (HPC). The solutions leverage Dell infrastructure along with partner technologies and are designed to address various analytical use cases such as digital manufacturing, life sciences research, and retail loss prevention.
Emerging Technology trends and employability skills
Iceberg, Right Ahead!!! discusses future-ready skills needed for a changing job market. It recommends developing T-shaped skills through emerging technologies like machine learning, cloud computing and edge analytics. Free online resources like MOOCs, tutorials and seminars can help gain both technical skills and soft skills like reading, writing and conferencing. New domains for AI include robotics, cybersecurity, media and entertainment. AI marketplaces allow buying and selling of algorithms and datasets. Edge computing processes data locally for lower latency. Robotic process automation automates repetitive tasks while AI handles complex tasks using learning. GPUs and other hardware accelerate deep learning.
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Hussein Osman, Segment Marketing Director at Lattice Semiconductor, and Richard Crisp, Vice President and Chief Scientist at Etron Technology America, co-presents the “Squeezing the Last Milliwatt and Cubic Millimeter from Smart Cameras Using the Latest FPGAs and DRAMs” tutorial at the May 2024 Embedded Vision Summit.
Attaining the lowest power, size and cost for a smart camera requires carefully matching the hardware to the actual application requirements. General-purpose media processors may appear attractive and easy to use, but often include unneeded features which increase system size, weight, power and cost. “Right-sizing” the camera design for the application requirements can save significant power, cost, size and weight.
In this talk, Osman and Crisp show how you can leverage an advanced power-optimized FPGA incorporating a soft RISC-V core combined with a video-bandwidth, low-pin-count DRAM to cut power consumption roughly in half for endpoint smart cameras used in automotive, industrial and other applications. They examine techniques for reducing power, cost and size including system architecture, memory architecture, packaging, and signaling and termination schemes. They also explore techniques for enhancing system reliability."Maximize Your AI Compatibility with Flexible Pre- and Post-processing," a Pr...
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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.
Embedded FPGA IP is highly efficient for pre- and post-processing data and can implement a variety of signal processing tasks such as image signal processing (defective pixel and color correction, for example), packet processing from network interfaces and signal processing from data converters (filtering). Additionally, this IP can manage data movement in and out of your AI engine as well as provide an adaptable protocol layer to connect to a variety of external interfaces, like USB and MIPI cameras. Flex Logix eFPGA IP is easy to integrate, high performing, lightweight and supported across more process nodes than any other supplier’s.“Addressing Tomorrow’s Sensor Fusion and Processing Needs with Cadence’s Newe...
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Amol Borkar, Product Marketing Director at Cadence, presents the “Addressing Tomorrow’s Sensor Fusion and Processing Needs with Cadence’s Newest Processors” tutorial at the May 2024 Embedded Vision Summit.
From ADAS to autonomous vehicles to smartphones, the number and variety of sensors used in edge devices is increasing: radar, LiDAR, time-of-flight sensors and multiple cameras are more and more common. And, as sensors have improved, the data rates associated with them have also increased. Traditionally, a dedicated processor has been utilized to process data from each sensor independently. Today, however, there is a growing need for a single, unified processor capable of processing multimodal sensor data utilizing both classical and AI algorithms and implementing sensor fusion for robust perception.
In this talk, Borkar introduces the new Vision 341 DSP and Vision 331 DSP from Cadence. These cores provide a versatile single-DSP solution for various workloads, including image sensing, radar, LiDAR and AI tasks.He explores the architecture of these new processors, highlights their performance and efficiency and outlines the associated developer tools and software building blocks.“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.“Silicon Slip-ups: The Ten Most Common Errors Processor Suppliers Make (Numbe...
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Phil Lapsley, Co-founder and Vice President of BDTI, presents the “Silicon Slip-ups: The Ten Most Common Errors Processor Suppliers Make (Number Four Will Amaze You!)” tutorial at the May 2024 Embedded Vision Summit.
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Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.“How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge,...
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Stephen Su, Senior Segment Marketing Manager at Arm, presents the “How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge has been transforming over the last few years, with newer use cases running more efficiently and securely. Most edge AI workloads were initially run on CPUs, but machine learning accelerators have gradually been integrated into SoCs, providing more efficient solutions. At the same time, ChatGPT has driven a sudden surge in interest in transformer-based models, which are primarily deployed using cloud resources. Soon, many transformer-based models will be modified to run effectively on edge devices.
In this presentation, Su explains the role of transformer-based models in vision applications and the challenges of implementing transformer models at the edge. Next, he introduces the latest Arm machine learning solution and how it enables the deployment of transformer-based vision networks at the edge. Finally, he shares an example implementation of a transformer-based embedded vision use case and uses this to contrast such solutions with those based on traditional CNN networks.“Nx EVOS: A New Enterprise Operating System for Video and Visual AI,” a Prese...
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Nathan Wheeler, Co-founder and CEO of Network Optix, presents the “Nx EVOS: A New Enterprise Operating System for Video and Visual AI” tutorial at the May 2024 Embedded Vision Summit.
In most software domains, developers don’t write code at the bare-metal level; they build applications on top of operating systems, which provide commonly needed functionality. Yet, today, developers of video and AI applications are effectively writing their applications at the bare-metal level, building the “plumbing” themselves to handle basics like device discovery, storage management, security and model deployment. These developers need an operating system that supports their applications so they can focus on what really matters: the core functionality of their product.
Nx EVOS is the world’s first enterprise video operating system. EVOS revolutionizes video management, offering device discovery, bandwidth optimization and security features—in cloud and on device. Its support for AI pipelines and user management enables scalable deployment of AI applications across environments and platforms, and it’s trusted by leading organizations such as SpaceX. In this presentation, you’ll learn how Nx EVOS can save you time and effort in building your next vision product.“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
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Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation."OpenCV for High-performance, Low-power Vision Applications on Snapdragon," a...
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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.
In this talk, Zhong introduces a new element of Qualcomm’s computer vision tools suite: a version of OpenCV optimized for Snapdragon platforms, which allows developers to leverage and port their existing OpenCV-based applications seamlessly to Snapdragon platforms. Supporting OpenCV v4.x and later releases, this implementation contains unique Qualcomm-specific accelerations of OpenCV and OpenCV extension APIs. Zhong explains how this library enables developers to leverage existing OpenCV code to achieve superior performance and power savings on Snapdragon platforms.“Deploying Large Models on the Edge: Success Stories and Challenges,” a Prese...
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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.
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.“Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment,...
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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.
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.“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
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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.
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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.“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
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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.
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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.“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
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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.“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
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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.
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.“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
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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.
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.More from Edge AI and Vision Alliance (20)
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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This talk covers:
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Reverse engineering and runtime analysis of Mobile malware.
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
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Paper Link: https://eprint.iacr.org/2024/257
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Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
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The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
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Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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Astute Business Solutions | Oracle Cloud Partner |
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
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Essentials of Automations: Exploring Attributes & Automation Parameters
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May 2018 Embedded Vision Summit Vision Tank Start-up Competition Finalist Presentation: Boulder AI
- 1. 1 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. AI vision at the edge. + AI
- 2. 2 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Smart City Boulder AI
- 3. 3 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Smart City Boulder AI
- 4. 4 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Activity Monitoring Boulder AI
- 5. 5 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Activity Monitoring Boulder AI Average “Smart cam”: Count People Edge-computing Intelligent Camera: Detect Gestures and Activities
- 6. 6 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. DNNcam Boulder AI
- 7. 7 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Image Processing Real-time 120 FPS High definition resolution NVIDIA TX2 GPU Support DNNCam (Capture) Tokenized Vision Data 23-segment human body GPU accelerated DNN-trained WRNCH model BodySLAM (Tracking) BoulderAI Edge Platform Enable Vision-as-a-Service (VaaS) Deploy OpenCV GPU accelerated ML model for anomaly in body action sequence Anomaly Detection Behavior Identification GPU accelerated DNN-based human action model Real-time event communication Skimmer attempts
- 8. 8 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. www.boulderai.com Hardware and software components are integrated into a single platform optimized for video AI applications The BoulderAI Edge Platform reduces the cost and complexity of designing, testing, and deploying the next-generation edge AI systems
- 9. 9 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Backup
- 10. 10 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. The Team Boulder AI
- 11. 11 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. Wrnch AI Project Partners/Collaborators Boulder AI Blinker View into the Blue Azure IoT Lab DeepvisionAI RadiusIoT KickView Real-time AI NVIDIA
- 12. 12 www.BoulderAI.com © 2018 Boulder AI LLC. All Rights Reserved. DNNCam Technicals ● Camera: 4k, 10-bit HDR, 60FPS ● Lens: motorized variable focus, zoom, iris, and IR filter. Low distortion, 4k resolution. ● Comm: Wifi 802.11AC, Bluetooth, CANbus, 2x USB3.0, Gb Ethernet, 2 Uarts, SPI, I2C, Satellite ● Storage: 32GB onboard flash + MicroSD expandable storage ● Power: 7-15W, 12-60V input for solar or industrial power. ● External LED lighting: Pulse synced LED lighting outputs with camera ● Dimensions: Approximately 2.5″ diameter, 6″ long ● Environmental: IP67 case rating, operating temp range - 20°F to 106°F ● Passive Cooling: No cooling fans to get fouled or damaged ● Made for Developers: DNNCam includes Nvidia TX2 module, widely supported by open source development community