Using the GREMLIN for digital FIR networks

Abstract

Time-delay neural networks are well-suited for prediction purposes. A particular implementation is the Finite Impulse Response neural net. The GREMLIN architecture is introduced to accommodate such networks. It can be micropipelined to achieve a 85 MCPS performance on a conventional connection-serial structure and allows from its Logic-Enhance Memory nature an easily parametrized design. A typical design for biomedical applications can be trained in a Cascade fashion and subsequently mapped.