“MIPI CSI-2 Image Sensor Interface Standard Features Enable Efficient Embedded Vision Systems,” a Presentation from the MIPI Alliance

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“Introduction to the CSI-2 Image Sensor Interface Standard,” a Presentation from the MIPI Alliance

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“Practical Approaches to DNN Quantization,” a Presentation from Magic Leap

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“Optimizing Image Quality and Stereo Depth at the Edge,” a Presentation from John Deere

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CircuitSutra provides SoC modeling services and supports its customers in adopting SystemC-based shift-left ESL methodologies. These methodologies enable hardware/software co-design, pre-silicon firmware development through virtual prototypes, architecture optimization for variables such as performance and power consumption, and high level synthesis.

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“Using a Collaborative Network of Distributed Cameras for Object Tracking,” a Presentation from Invision AI

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ProHawk’s patented algorithms and technologies enable real-time, pixel-by-pixel video restoration, overcoming virtually all environmental obstacles to maximize end user insight and productivity. ProHawk’s software works within less than a blink of an eye (less than 3 milliseconds) allowing greater and earlier object detection, positioning the company to capture a meaningful share of the $135B computer vision market.

The ProHawk Technology Group has recently partnered with Dell, NVIDIA, and select other companies to bring technologies to market across a range of applications, including medical, military, law enforcement, safety and security, and other industries.

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On the software front, DeGirum continues to prioritize user experience and adaptability. The company has launched a user-friendly Python SDK and will soon launch a JavaScript SDK. The upcoming SDK promises seamless real-time operations directly from browsers, a testament to DeGirum’s commitment to enhancing accessibility and ease-of-use for developers worldwide.

In the demo, Kudryavtsev showcases DeGirum’s JavaScript SDK executing Yolo-based face detection directly from the browser using a local AI accelerator. Concurrently, he demonstrates the company’s Python SDK running Yolo-based hand detection via the cloud. Both SDKs assist with the preprocessing and postprocessing tasks, as well as improving efficiency. And both implementations utilize local camera feeds and display real-time processing.

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The first demonstration is of people detection and tracking, performed on MediaTek’s Genio 500 SoC. As part of the demo, the camera captures an image and a person is detected using an objection detection AI network, followed by an OpenPose network which in realtime is able to overlay a skeletal representation of the person’s pose. The AI computation required for this demo is performed on MediaTek’s integrated APU (AI Processing Unit), which is comprised of 2x Tensilica Vision P6 DSPs. Additionally, a servo controlled articulating arm moves the camera to actively track the person’s movement.

The second demonstration is of a large image language model in an industrial camera, provided by Labforge.ca. Unlike typical LLM demos, this demo takes images and text prompts as input and provides a confidence level that the prompts match the captured image in realtime. The “Bottlenose” camera system features an SoC by Toshiba that integrates 4x Tensilica Vision P6 DSPs to help assist and accelerate the performance of today’s generative AI models. The Vision P6 DSP handles pre/post-processing, various CV/imaging algorithms, and multiple layers of the neural network.

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“A Survey of Model Compression Methods,” a Presentation from Instrumental

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