Analog VLSI and neural systems
Analog VLSI and neural systems
A bidirectional analog VLSI Cochlear model
Proceedings of the 1991 University of California/Santa Cruz conference on Advanced research in VLSI
VLSI analogs of neuronal visual processing: a synthesis of form and function
VLSI analogs of neuronal visual processing: a synthesis of form and function
Design of analog-digital VLSI circuits for telecommunications and signal processing
Analog VLSI signal processing: why, where, and how?
Journal of VLSI Signal Processing Systems - Joint special issue on Analog VLSI computation; also see Analog Integrated Circuits Signal Process., Vol. 6, No. 1
Analog Integrated Circuits and Signal Processing - Special issue: low-voltage low-power analog integrated circuits
Place coding in analog VLSI: a neuromorphic approach to computation
Place coding in analog VLSI: a neuromorphic approach to computation
Neuromorphic systems engineering: neural networks in silicon
Neuromorphic systems engineering: neural networks in silicon
Pseudo-Resistive Networks and their Applications to Analog Collective Computation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Low-power analog fuzzy rule implementation based on a linear MOS transistor network
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
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Biology can be a rich source of inspiration for engineers. In particular, designers of VLSI processing circuits and systems can draw inspiration from several aspects of the brain. Inspired from evolution, the opportunistic exploitation of all the properties of the technology provides very efficient analog circuit techniques. The collective computation carried out by the brain in its massively parallel architecture can be emulated on silicon. Strategies like learning and adaptation are very beneficial to VLSI processing. The same is true for the unusual ways used by the brain to represent signals and information. Four industrial chips developed with this bio-inspired approach are described, as well as several experimental circuits that demonstrate its potential for future products.