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CameraLink IP for high speed, high bandwidth Image acquisition

CameraLink IP for high speed, high bandwidth Image acquisition

by Kamalesh Vikramasimhan | Jun 24, 2020 | Embedded Vision

Introduction CameraLink is the best suited interface standard for real time, low latency, high bandwidth applications. CameraLink enables transmission of high-resolution image data at high speeds for both line scan and area scan cameras. The interface has a...
Introducing FPGA Integrated Camera module from Falcon Series

Introducing FPGA Integrated Camera module from Falcon Series

by Kamalesh Vikramasimhan | Jun 24, 2020 | Embedded Vision

We are proud to present YantraVision’ s newest range of products, the Falcon Series. The Falcon product portfolio consists of FPGA Integrated Machine Vision products that are used for high speed hard real-time applications with cycle time in microseconds. The FPGA...
Pynq – BNN for Fabric Texture Analysis on the EDGE – Part 1

Pynq – BNN for Fabric Texture Analysis on the EDGE – Part 1

by Kamalesh Vikramasimhan | Dec 31, 2019 | Case Study, FPGA Capability

This blog series is split into 3 parts – Part 1: Why Binary Neural Networks Part 2: Why PYNQ? Part 3: Pynq – BNN Implementation Introduction We develop High Speed Embedded Vision modules for use cases in the Textile Industries. These high-speed modules are...
TRD Design for Xilinx EDGE AI on ZCU 106

TRD Design for Xilinx EDGE AI on ZCU 106

by Kamalesh Vikramasimhan | Dec 21, 2019 | Case Study, FPGA Capability

Recap: Deep Learning on Xilinx Edge AI (DPU Implementation) – Part 1 Introduction In the course of developing Edge AI solutions, it is imperative to evaluate the solution on a standard Xilinx Evaluation board to see if your model fits on the silicon and you are able...
TRD Design for Xilinx EDGE AI on ZCU 106

Deep Learning on Xilinx Edge AI (DPU Implementation) – Part 1

by Kamalesh Vikramasimhan | Dec 18, 2019 | Case Study

Introduction Many applications like smart manufacturing, surveillance and augmented reality require Artificial Intelligence to be implemented on the EDGE. Some of the challenges in implementing AI on the EDGE are – Achieving lowest possible latency Network pruning to...
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