Learning on Silicon

Adaptive VLSI Neural Systems

Inbunden, Engelska, 1999

AvG. Cauwenberghs,Magdy A. Bayoumi,Magdy Bayoumi

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This publication combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of learning on silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. It serves as a reference for beginners and experienced researchers alike. It provides material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.

Produktinformation

Utgivningsdatum1999-06-30
Mått155 x 235 x 29 mm
Vikt828 g
FormatInbunden
SpråkEngelska
SerieSpringer International Series in Engineering and Computer Science
Antal sidor426
Upplaga1999
FörlagKluwer Academic Publishers
ISBN9780792385554

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Preface. Acknowledgements. 1. Learning on Silicon: A Survey; G. Cauwenberghs. Part I: Adaptive Sensory Processing. 2. Adaptive Circuits and Synapses using pFET Floating-Gate Devices; P. Hasler, et al. 3. Silicon Photoreceptors with Controllable Adaptive Filtering Properties; S.-C. Liu. 4. Analog VLSI System for Active Drag Reduction; V. Koosh, et al. Part II: Neuromorphic Learning. 5. Biologically-inspired Learning in Pulsed Neural Networks; T. Lehmann, R. Woodburn. 6. Spike Based Normalizing Hebbian Learning in an Analog VLSI Artificial Neuron; P. Hafliger, M. Mahowald. 7. Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree; W.C. Westerman, et al. Part III: Learning Architecture. 8. ART1 and ARTMAP VLSI Circuit Implementation; T. Serrano-Gotarredona, B. Linares-Barranco. 9. Circuits for On-Chip Learning in Neuro-Fuzzy Controllers; F. Vidal-Verdu, et al. 10. Analog VLSI Implementation of Self-learning Neural Networks; T. Morie. 11. A 1.2 GFLOPS Neural Network Processor for Large-Scale Neural Network Accelerator Systems; Y. Kondo, et al. Part IV: Learning Dynamics. 12. Analog Hardware Implementation of Continuous-Time Adaptive Filter Structures; J.G. Harris, et al. 13. A Chip for Temporal Learning with Error Forward Propagation; F.M. Salam, H.-J. Oh. 14. Analog VLSI On-Chip Learning Neural Network with Learning Rate Adaptation; G.M. Bo, et al. Part V: Learning Systems. 15. Learning on CNN Universal Machine Chips; R. Carmona, et al. 16. Analog VLSI Parallel Stochastic Optimization for Adaptive Optics; R.T. Edwards, et al. 17. A Nonlinear Noise-Shaping Delta-Sigma Modulator with On-Chip Reinforcement Learning; G. Cauwenberghs. 18. A Micropower Adaptive Linear Transform Vector Quantiser; R.J. Coggins, et al. Index.