Learning on Silicon

Adaptive VLSI Neural Systems
1999
ISBN: 978-0-7923-8555-4
Verlag: Springer US

Buch, Englisch, Band 512, 426 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1770 g

Reihe: The Springer International Series in Engineering and Computer Science

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 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.
As the first comprehensive treatment on the subject, serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.

Zielgruppe

Research

Autoren/Hrsg.

Bayoumi, Magdy A.

Cauwenberghs, G.

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Mikroprozessoren
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

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. Häfliger, 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-Verdú, 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.