CMU Artificial Intelligence Repository
MBP: Matrix Backpropagation Package
MBP (Matrix Back Propagation) is an efficient implementation of the
back-propagation algorithm for current-generation workstations. The
algorithm includes a per-epoch adaptive technique for gradient
descent. All the computations are done through matrix multiplications
and make use of highly optimized C code. The goal is to reach almost
peak-performances on RISCs with superscalar capabilities and fast
caches. On some machines (and with large networks) a 30-40x speed-up
can be measured respect to conventional implementations.
as the files MBPv1.1.tar.Z (unix version) and
MBPv11.zip (DOS version)
Version: 1.1 (23-NOV-93)
Requires: C, UNIX
CD-ROM: Prime Time Freeware for AI, Issue 1-1
Author(s): Davide Anguita
University of Genova
Via all'Opera Pia 11a
16145 Genova, ITALY
Authors!Anguita, Backpropagation, Gradient Descent, MBP,
Machine Learning!Neural Networks,
Matrix Backpropagation Package, Matrix Multiplication,
Neural Networks, Univ. of Genova
The documentation is included in the distribution as the postscript
D.Anguita, G.Parodi, R.Zunino - An efficient implementation of BP on RISC-
based workstations. Neurocomputing, in press.
D.Anguita, G.Parodi, R.Zunino - Speed improvement of the BP on current
generation workstations. WCNN '93, Portland.
D.Anguita, G.Parodi, R.Zunino - YPROP: yet another accelerating technique
for the bp. ICANN '93, Amsterdam.
Last Web update on Mon Feb 13 10:25:26 1995