Using PHiPAC to speed error back-propagation learning

We introduce PHiPAC, a coding methodology for developing portable high-performance numerical libraries in ANSI C. Using this methodology, we have developed code for optimized matrix multiply routines. These routines can achieve over 90% of peak performance on a variety of current workstations, and are often faster than vendor-supplied optimized libraries. We then describe the bunch-mode back-propagation algorithm and how it can use the PHiPAC derived matrix multiply routines. Using a set of plots, we investigate the tradeoffs between bunch size, convergence rate, and training speed using a standard speech recognition data set and show how use of the PHiPAC routines can lead to a significantly faster back-propagation learning algorithm