LETTER FROM THE EDITOR |
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Dear Colleague, Did you know? Last year, almost 60% of vision-based product developers were using or planning to use 3D perception. That’s just one finding from last year’s Computer Vision and Perceptual AI Developer Survey. We’d love it if you’d take this year’s survey to tell us about your use of processors, tools and algorithms in CV and perceptual AI. In exchange, you’ll get access to detailed results and a $250 discount on a two-day pass to the Embedded Vision Summit in May. Click here to take the survey now! Brian Dipert |
TECHNOLOGY TUTORIALS |
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An Introduction to Semantic Segmentation Vision applications often rely on object detectors, which determine the nature and location of objects in a scene. But many vision applications require a different type of visual understanding: semantic segmentation. Semantic segmentation classifies each pixel of an image, associating each pixel with an object class (e.g., pavement, pedestrian). This is required for separating foreground objects from background, for example, or for identifying drivable surfaces for autonomous vehicles. A related type of functionality is object segmentation, which associates each pixel with a specific object (e.g., pedestrian #4), and panoptic segmentation, which combines the functionality of semantic and instance segmentation. In this talk, Sébastien Taylor, Vice President of Research and Development for Au-Zone Technologies, introduces deep learning-based semantic, instance and panoptic segmentation. He explores the network topologies commonly used and how they are trained. He discusses metrics for evaluating segmentation algorithm output and considerations when selecting segmentation algorithms. Finally, he identifies resources useful for developers getting started with segmentation. |
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An Introduction to Visual Simultaneous Localization and Mapping (VSLAM) Simultaneous localization and mapping (SLAM) is widely used in industry and has numerous applications where camera location and motion need to be accurately determined. In this presentation, Cadence’s Amol Borkar, Product Marketing Director, and Shrinivas Gadkari, Design Engineering Director, give an introduction to visual SLAM, explain how it works and explore some of the key components of a typical SLAM algorithm along with some of the common challenges in creating and using SLAM solutions. They also dive into some of the latest SLAM trends. |
ADAPTABLE COMPUTING |
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Maximize Your AI Compatibility with Flexible Pre- and Post-processing 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 from Jayson Bethurem, Vice President of Marketing and Business Development at Flex Logix, 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 (for example, defective pixel and color correction), packet processing from network interfaces and processing signals from data converters (for example, 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. |
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Meeting the Critical Needs of Accuracy, Performance and Adaptability in Embedded Neural Networks In this talk, Aman Sikka, Chief Architect at Quadric, explores the challenges of accuracy and performance when implementing quantized machine learning inference algorithms on embedded systems. He explains how the thoughtful use of fixed-point data types yields significant performance and efficiency gains without compromising accuracy. And he explores the need for modern SoCs to not only efficiently run current state-of-the-art neural networks but also to be able to adapt to future algorithms. This requires industry to shift away from the approach of adding custom fixed-function accelerator blocks adjacent to legacy architectures and toward embracing flexible and adaptive hardware. This hardware flexibility not only allows SoCs to run new networks, but also enables ongoing software and compiler innovations to explore optimizations such as better data layout, operation fusion, operation remapping and operation scheduling without being constrained by a fixed hardware pipeline. |
UPCOMING INDUSTRY EVENTS |
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Leveraging Synthetic Data for Real-time Visual Human Behavior Analysis Using the SKY ENGINE AI Platform – SKY ENGINE AI Webinar: September 26, 2024, 9:00 am PT Delivering High Performance, Low Power Complete Edge-AI Applications with the SiMa.ai One Platform MLSoC and Toolset – SiMa.ai Webinar: October 17, 2024, 9:00 am PT Embedded Vision Summit: May 20-22, 2025, Santa Clara, California |
FEATURED NEWS |
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Nota AI and Advantech Partner to Advance the On-device Generative AI Market Upcoming Webinar from Ceva and Visionary.ai Explores How AI Enables Cameras to See In the Dark FRAMOS and Antmicro Join Forces to Enable Rapid Development of Open Source-based Embedded Vision Systems Ambarella Advances Intelligent Driving Development in Coordination with Plus and Leapmotor SiMa.ai, Lanner, and AWL Collaborate to Accelerate Smart Retail at the Edge |
EDGE AI AND VISION PRODUCT OF THE YEAR WINNER SHOWCASE |
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Tenyks Data-Centric CoPilot for Vision (Best Edge AI Developer Tool) Tenyks’ Data-Centric CoPilot for Vision is the 2024 Edge AI and Vision Product of the Year Award Winner in the Edge AI Developer Tools category. The Data-Centric CoPilot for Vision platform helps computer vision teams develop production-ready models 8x faster. The platform enables machine learning (ML) teams to mine edge cases, failure patterns and annotation quality issues for more accurate, capable and robust models. In addition, it helps ML teams intelligently sub-sample datasets to increase model quality and cost efficiency. The platform supports the use of multimodal prompts to quickly compare model performance on customized training scenarios, such as pedestrians jaywalking at dusk, in order to discover blind spots and enhance reliability. ML teams can also leverage powerful search functionality to conduct data curation in hours vs weeks. One notable feature of the platform is its multimodal Embeddings-as-a-Service (EaaS) to expertly organize, curate, and manage datasets. Another key platform feature is the streamlined cloud integration, supporting a multitude of cloud storage services and facilitating effortless access and management of large-scale datasets. Please see here for more information on Tenyks’ Data-Centric CoPilot for Vision. The Edge AI and Vision Product of the Year Awards celebrate the innovation of the industry’s leading companies that are developing and enabling the next generation of edge AI and computer vision products. Winning a Product of the Year award recognizes a company’s leadership in edge AI and computer vision as evaluated by independent industry experts. |
