Architecture and implementation of a Restricted Boltzmann Machine for handwritten digits recognition
Nikolaos Toulgaridis, Eleni Bougioukou, Theodore A. Antonakopoulos · 2017
Restricted Boltzmann Machines are artificial neural networks used in many types of statistical classification. In this work we present the architecture and implementation of such a neural network for fast recognition of hand-written digits. We use fixed and floating point arithmetic for minimizing the required hardware resources, and the use of pipeline results to a processing rate of more than 1 Mimages/sec per RBM. Four neural networks have been used on a PCIe-based hardware accelerator that uses a Virtex-7 FPGA, and that results to a total processing rate of more than 4 Mimages/sec.