YantraVision Blog | FPGA Implementation for Image processing

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...

PYNQ on ZYNQ

by Kamalesh Vikramasimhan | Jun 20, 2019 | FPGA Capability

PYNQ, Python Productivity for Zynq, makes it very easy for designers of embedded system to use XILINX Zynq devices without having to master complex design tools to design programmable logic circuits. PL as Hardware Libraries Programmable logic circuits are presented...

Using HLS to implement Image Processing (Part 2: Resource Optimization)

by Kamalesh Vikramasimhan | Jun 13, 2019 | FPGA Capability

Part 1: Using HLS for Image Processing (Introduction) Design space exploration involves exploring different combinations to achieve optimal trade-of between speed and resources. Within HLS, software profiling tools can help to identify processing bottlenecks, enabling...

Using HLS to implement Image Processing (Part 1: Introduction)

by Kamalesh Vikramasimhan | Jun 6, 2019 | FPGA Capability

High level synthesis (HLS) tools, like XILINX Vivado HLS, can provide significant benefits for implementing algorithms like signal processing and image processing on FPGAs. These tools enable development and testing of hardware-based algorithms using higher level...

Using FPGA for High Speed Image Processing – Part 2: Exploiting Parallelism

by Venkata Sarathy | May 30, 2019 | FPGA Capability

Recap: Using FPGA for High Speed Image Processing Part 1: Introduction Parallelism in FPGA By default, image processing is inherently parallel and the ability to exploit this parallelism has significant implications when building embedded vision systems. This...

Using FPGA for High Speed Image Processing – Part 1: Introduction

by Kamalesh Vikramasimhan | May 24, 2019 | FPGA Capability

Embedded Vision systems are known to be quick and efficient. These systems can be built using a system on chip (SoC) controller based on ARM architecture or on a field-programmable gate array (FPGA) architecture. In general, systems built on ARM based SoC implement...

Next Entries »

TRD Design for Xilinx EDGE AI on ZCU 106

PYNQ on ZYNQ

Using HLS to implement Image Processing (Part 2: Resource Optimization)

Using HLS to implement Image Processing (Part 1: Introduction)

Using FPGA for High Speed Image Processing – Part 2: Exploiting Parallelism

Using FPGA for High Speed Image Processing – Part 1: Introduction

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