A scalable low voltage analog Gaussian radial basis circuit

Gaussian basis function (GBF) networks are powerful systems for learning and approximating complex input-output mappings. Networks composed of these localized receptive field units trained with efficient learning algorithms have been simulated solving a variety of interesting problems. For real-time and portable applications however, direct hardware implementation is needed. We describe experimental results from the most compact, low voltage analog Gaussian basis circuit yet reported. We also extend our circuit to handle large fan-in with minimal additional hardware. Our design is hierarchical and the number of transistors scales almost linearly with the input dimension making it amenable to VLSI implementation.

[1]  John C. Platt,et al.  An Analog VLSI Chip for Radial Basis Functions , 1992, NIPS.

[2]  T. Delbruck 'Bump' circuits for computing similarity and dissimilarity of analog voltages , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[4]  Bing J. Sheu,et al.  A Gaussian synapse circuit for analog VLSI neural networks , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[5]  A. Murray,et al.  Programmable analogue VLSI for radial basis function networks , 1993 .

[6]  Paul M. Chau,et al.  A radial basis function neurocomputer implemented with analog VLSI circuits , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[7]  Paul E. Hasler,et al.  Single transistor learning synapse with long term storage , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.