An FPGA implementation of a Restricted Boltzmann Machine classifier using stochastic bit streams
Bingzhe Li, M. Hassan Najafi, David J. Lilja · 2015
Artificial neural networks (ANNs) usually require a very large number of computation nodes and can be implemented either in software or directly in hardware, such as FPGAs. Software-based approaches are offline and not suitable for real-time applications, but they support a large number of nodes. FPGA-based implementations, in contrast, can greatly speedup the computation time. However, resource limitations in an FPGA restrict the maximum number of computation nodes in hardware-based approaches. This work exploits stochastic bit streams to implement the Restricted Boltzmann Machine (RBM) handwritten digit recognition application completely on an FPGA. Exploiting this approach saves a large number of hardware resources making the FPGA-based implementation of large ANNs feasible.