In recent years, neural networks have proven to be algorithms that can solve a wide variety of problems. They are used in particular when there is a lot of data but no formal description of the problem. The mathematical structure of neural networks allows for a high degree of parallelization and the use of bi-width optimizations. They are therefore well suited for implementation on FPGAs. This means that the advantages of AI can also be used in the embedded sector.
The process for implementing a neural network in the FPGA includes, among other things:
- Data preparation
- Prototyping and network design with python machine learning frameworks
- Offline training of the network with data records
- Transferring the architecture into an FPGA design
- Interface specification
With the help of FPGAs, you can also use machine learning algorithms where low latencies and performance requirements are needed.