How fast is an fpga in image processing

In our real-time experience with one of the clients in the high-speed sorting OEM industry, the available cycle time starting from the sorting decision to actuation of the pneumatic ejectors was only 3 ms.

The original design included microcontrollers that were only capable of performing simple thresholding techniques within the stipulated cycle time.

This restricted the quality of inspection on the end products. We implemented an image processing algorithm with a FPGA processor exploiting parallelism to achieve more complex image processing within the same cycle time. Click here share case study link to read the case study in detail.

Your email address will not be published. Phone Number. Company Careers Enquire now. Real-time Image Processing Real-time image processing systems help users to capture images, analyse them and use the result to control a specific activity within a specific cycle time.

Case Study: Embedded Vision Using FPGA Processor In our real-time experience with one of the clients in the high-speed sorting OEM industry, the available cycle time starting from the sorting decision to actuation of the pneumatic ejectors was only 3 ms. Submit a Comment Cancel reply Your email address will not be published. Download Chromasens Brochure. Download Mikrotron Eosens Brochure. Download Falcon Brochure. How fast is an FPGA in image processing? Abstract: In image processing, FPGAs have shown very high performance in spite of their low operational frequency.

This high performance comes from 1 high parallelism in applications in image processing, 2 high ratio of 8 bit operations, and 3 a large number of internal memory banks on FPGAs which can be accessed in parallel. In the recent micro processors, it becomes possible to execute SIMD instructions on bit data in one clock cycle.

Embedded vision systems need to be highly compact and function in highly challenging and unstructured environments, while still delivering high quality images. Because of this, their processing architecture differs from what is found in most machine vision systems.

While embedded vision is still an emerging technology, to date there are typically two main types of processors used in embedded systems — field programmable gate arrays FPGAs and graphics processing units GPUs. GPUs are widely used in embedded vision systems because they are capable of delivering large amounts of parallel computing potential.

This may even include accelerating key portions of the processing pipeline that deal with pixel data. This is particularly useful in high resolution or high speed applications where enormous amounts of image data is generated. All GPUs leverage software for imaging algorithms.