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A Fast Kohonen Net Implementation for Krste Asanovi c
 

Summary: A Fast Kohonen Net Implementation for
Spert-II
Krste Asanovi c
Computer Science Division
Department of Electrical Engineering and Computer Sciences
University of California at Berkeley
Berkeley, CA 94720-1776
Abstract. We present an implementation of Kohonen Self-Organizing
Feature Maps for the Spert-II vector microprocessor system. The im-
plementation supports arbitrary neural map topologies and arbitrary
neighborhood functions. For small networks, as used in real-world tasks,
a single Spert-II board is measured to run Kohonen net classi cation at
up to 208 million connections per second MCPS. On a speech coding
benchmark task, Spert-II performs on-line Kohonen net training at over
100 million connection updates per second MCUPS. This represents
almost a factor of 10 improvement compared to previously reported im-
plementations. The asymptotic peak speed of the system is 213 MCPS
and 213 MCUPS.
1 Introduction
Spert-II is a workstation accelerator constructed around the T0 vector micropro-

  

Source: AsanoviŠ, Krste - Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)

 

Collections: Computer Technologies and Information Sciences