Evolving classifiers on field programmable gate arrays: Migrating XCS to FPGAs

Abstract

The paper presents the first results of the prototype implementation of the eXtended learning Classifier System (XCS) in hardware and precisely on Field Programmable Gate Arrays. For this purpose we introduce a version of the XCS classifier system completely based on integer arithmetic, that we name XCSi, instead of the usual floating point one, to exploit the peculiarities and overcome the limitations of the hardware platform. We present an analysis of XCSi performance and the guidelines for a hardware implementation, showing that, although there is a dramatic reduction of available precision, the integer version of XCS can reach optimal performance in all problems considered, though it often converges more slowly than the original floating point version. Guidelines for a hardware implementation are provided, by analyzing how XCSi functional components can be designed on an FPGA.