A Scalable Processor Array for Self-Organizing Feature Maps

Rüping S, Rückert U. A Scalable Processor Array for Self-Organizing Feature Maps. In: Proceedings of the 6th International Conference on Microelectronics for Neural Networks and Fuzzy Systems (MicroNeuro). Lausanne, Switzerland: IEEE Computer Society Press; 1996.Selforganizing Feature Maps (SOFMs) can be applied for data analysis, controlling problems and pattern matching. In many cases the requirements of a system using these maps are high performance and small physical size. This leads to the necessity of custom chip designs. In this paper two chips are presented, that realize a scalable processor array for self-organizing feature maps. First the design and test results of a single processor chip are described. Based on these results a second chip has been developed implementing a 5 by 5 array of elements. Each processor has on-chip memory to store 64 weights of 8 bit. The calculation unit has an internal precision of 14 bit. An input pattern can have 64 vector components of 8 bit. In order to achieve high speed, all elements work in parallel. Several of this chips can be cascaded to larger map sizes in a syste