In this talk, the Caper team will explore deploying an embedded computer vision model on Android devices for real-time product classification with the goal of increased customer satisfaction during shopping. We will dive into the details of achieving real-time inference […]
As demand rises for vision capabilities in robotics, virtual/augmented reality, drones and automotive, there’s a growing need for systems to incorporate multiple cameras. However, the limited number of camera interfaces on SoCs poses a challenge. In this presentation, we will […]
Simultaneous localization and mapping (SLAM) is widely used in industry and has numerous applications where camera or ego-motion needs to be accurately determined. In this presentation, Amol Borkar and Shrinivas Gadkari will give an introduction to visual SLAM, explain how […]
In this intermediate-level presentation, we will delve into some of the most common problems that arise when training deep neural networks. We’ll provide a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score. […]
In this presentation, we will explore the challenges of accuracy and performance when implementing quantized machine learning inference algorithms on embedded systems. We’ll explain how the thoughtful use of fixed-point data types yields significant performance and efficiency gains without compromising […]
Optix is a computer vision-based AI system that measures advertising and media exposures on mobile devices for real-time marketing optimization. Optix was initially developed as a cloud-based solution, but costs and limitations associated with relying entirely on the cloud drove […]
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 session, learn how embedding FPGA technology can solve these problems—expanding your market access […]
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