CONF Moerland-96.1/IDIAP Hardware-Friendly Learning Algorithms for Neural Networks: An Overview Moerland, Perry Fiesler, Emile EXTERNAL https://publications.idiap.ch/attachments/reports/1996/hardware.pdf PUBLIC EPFL and CSEM - Proceedings of the Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems: MicroNeuro'96 Lausanne, Switzerland 1996 IEEE Computer Society Press Los Alamitos, CA 117-124 0-8186-7373-7 The hardware implementation of artificial neural networks and their learning algorithms is a fascinating area of research with far-reaching applications. However, the mapping from an ideal mathematical model to compact and reliable hardware is far from evident. This paper presents an overview of various methods that simplify the hardware implementation of neural network models. Adaptations that are proper to specific learning rules or network architectures are discussed. These range from the use of perturbation in multilayer feedforward networks and local learning algorithms to quantization effects in self-organizing feature maps. Moreover, in more general terms, the problems of inaccuracy, limited precision, and robustness are treated.