FPGA Frame Grabber-Based Deep Learning
If your deep learning application requires high throughput, such as time-critical use in production, the frame grabber-based vision system for deep learning is the right choice for you.
Deep learning in image processing
Our white paper presents the following topics in detail:
Application fields for deep learning in machine vision
Advantages of deep learning-based methods
Cost of using deep learning
Optimization of deep learning nets through hybrid approach
Application fields for solutions without deep learning
CNN on FPGAs with VisualApplets software
With our graphical FPGA development software VisualApplets, using CNNs on FPGAs has never been easier. Pre-trained CNN nets of varying size and complexity can be loaded directly onto an FPGA. The software supports pre-trained nets from the most common CNN libraries, such as TensorFlow. The nets can also be retrained with little effort. Additional image optimizations can be easily integrated as pre- or postprocessing steps.
More about VisualApplets
Let us help you to bring your CNN on an FPGA
For implementing FPGA-based deep learning applications, we offer a CNN run time license with two service packages, each geared to your level of experience with deep learning. For already trained networks, we offer support for FPGA implementation. For customers with less knowledge in deep learning, we are offering the complete design of the CNN, as well as the FPGA implementation for the desired bandwidth and accuracy – while your intellectual property stays within your organization.
To the frame grabber services
