Titlebook: Smart Adaptive Systems on Silicon; Maurizio Valle Book 2004 Springer Science+Business Media Dordrecht 2004 CMOS.Potential.Signal.Standard.

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How to Organize Analog VLSI Operators to Perform Perceptual Tasks in Machine Visionviour in space and spatial frequency domains, and can support a variety of embedded analog filtering operators in smart vision sensors. These networks can be mapped, very efficiently, on VLSI structures operating as an array of analog .. The adaptability required to tackle the hardware implementatio

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Supervised Learning Systems on Siliconlog on-chip implementations of supervised learning algorithms i.e. dedicated versions of gradient descent (i.e. Weight Perturbation) algorithms with the goal of implementing cognitive tasks on silicon. Smart adaptive systems can effectively take advantage of analog on-chip learning implementations w

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Evolvable Hardware in Silicon device was fabricated its structure was forever fixed. Reconfigurable hardware came as a second generation: new configurations could be downloaded changing the function of the device and also bypassing faulty areas, if any. The third generation is that of self-configurable, evolvable hardware (EHW)

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Mixed-Mode Neural Networks Adapted for Evolutionary Algorithmsg neural networks which usually lack an exact numerical model due to the unavoidable device variations introduced during manufacturing. Analog neural network implementations should take advantage of the progress of the semiconductor technology. This can be achieved by reducing analog neural networks

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Log-Domain Circuit Techniques for Nonlinear Neural Networks with Complex Dynamicsal behaviors. We have designed a log-domain hardware that implements the discrete space version of the selected reaction-diffusion equation [1]. The logarithmic compression of the state variables allows several decades of variation of these state variables within subthreshold operation of the MOS tr

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Remote Powering and Control of Intelligent Microsystemsich is an important field of research. Many applications are expected, particularly in the field of the microassembly and the test of circuits in confined environments [1]. This chapter describes the underlying principles and reports the successful asynchronous remote operation of two Distributed Mi

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Microsystemshe technology, are described and illustrated either with some block diagrams and microphotographs, in order to give an accurate idea of key aspects of the technology. Some advanced research achievement (e.g. the “smartdust”) and some techno-economical issues are then illustrated to depict a possible “future technology scenario”.

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Neuromorphic Engineeringory signals. In this chapter we present two representative case studies describing a single-chip vision sensor and a multi-chip processing network, that contain most of the characteristic elements found in today’s neuromorphic systems.