Analog VLSI and neural systems
Analog VLSI and neural systems
Adaptive inverse control
Adaptive Signal Processing in Mixed-Signal VLSI with Anti-Hebbian Learning
ISVLSI '06 Proceedings of the IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures
Tolerance to analog hardware of on-chip learning in backpropagation networks
IEEE Transactions on Neural Networks
Blind Source-Separation in Mixed-Signal VLSI Using the InfoMax Algorithm
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Image Recognition in Analog VLSI with On-Chip Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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Device mismatch, charge leakage and nonlinear transfer functions limit the resolution of analog-VLSI arithmetic circuits and degrade the performance of neural networks and adaptive filters built with this technology. We present an analysis of the impact of these issues on the convergence time and residual error of a linear perceptron using the Least-Mean-Square (LMS) algorithm. We also identify design tradeoffs and derive guidelines to optimize system performance while minimizing circuit die area and power dissipation.