Abstract:
We took the back-propagation algorithms of Werbos for recurrent and feed-forward neural networks and implemented them on machines with graphics processing units (GPU). The parallelism of these units gave our implementations a 10 to 100 fold increase in speed. For nets with less than 20 neurons the machine performed faster without using the GPU, but for larger nets use of the GPU always gave better times.
