Neural computing: theory and practice
Neural computing: theory and practice
Analog VLSI and neural systems
Analog VLSI and neural systems
VLSI Architectures for Neural Networks
IEEE Micro
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
Analog VLSI hardware implementation of a supervised learning algorithm
Hardware implementation of intelligent systems
Analog VLSI Implementation of Artificial Neural Networks with Supervised On-Chip Learning
Analog Integrated Circuits and Signal Processing
Design and Codesign of Neuro-Fuzzy Hardware
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
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The effects of silicon implementation on the backpropagation learning rule in artificial neural systems are examined. The effects on learning performance of limited weight resolution, range limitations, and the steepness of the activation function are considered. A minimum resolution of about 20/22 bits is generally required, but this figure can be reduced to about 14/15 bits by properly choosing the learning parameter eta which attains good performance in presence of limited resolution. This performance can be further improved by using a modified batch backpropagation rule. Theoretical analysis is compared with ad-hoc simulations and results are discussed in detail.