Using Stochastic Computing to Reduce the Hardware Requirements for a Restricted Boltzmann Machine Classifier
Bingzhe Li, M. Hassan Najafi, David J. Lilja · 2016
Artificial neural networks are powerful computational systems with interconnected neurons. Generally, these networks have a very large number of computation nodes which forces the designer to use software-based implementations. However, the software based implementations are offline and not suitable for portable or real-time applications. Experiments show that compared with the software based implementations, FPGA-based systems can greatly speed up the computation time, making them suitable for real-time situations and portable applications. However, the FPGA implementation of neural networks with a large number of nodes is still a challenging task.