AI and Machine Learning with FPGAs

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🚧 Documentation under development

The FPGA Minutes to Become an Expert guide is currently under active development. Some sections may be incomplete or change without notice.

Questions? Contact RidgeRun or email to support@ridgerun.com.

FPGA Minutes to Become an Expert
Introduction
What is an FPGA? FPGAs and their applications Popular Vendors Development process When to Choose an FPGA over a GPU/CPU?
FPGA Knowledge
Synthesis Flows High Level Synthesis Vendor Specific Primitives
Xilinx FPGAs
Evaluation boards Minimum Design for KV260 Development workflow and tools Getting Started
Lattice FPGAs
Evaluation boards Development workflow and tools Getting Started
Simulation Tools
CocoTB
AI and Machine Learning
Introduction to AI Architectures and Frameworks Quantisation Pruning
Contact Us

This section covers the foundations of Deep Learning Inference with emphasis on FPGA acceleration. Multiple concepts are very well-known, given that they apply to the model and can be applied to different acceleration platforms. However, this section will focus on the effects of the FPGA implementations.

This section is divided as follows:

• Introductory topics to the AI implementations on FPGAs
  • Introduction to AI
  • Frameworks and Architectures
• Concepts and Optimisations
  • Quantisation
  • Pruning