Place Coding In Analog Vlsi
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Place Coding in Analog VLSI
Author: Oliver Landolt
language: en
Publisher: Springer Science & Business Media
Release Date: 1998-07-31
Microelectronics have been "bio-inspired" by the human brain to devise a new way, via topological maps, to represent and process data. In the world of fuzzy logic and artificial neural networks, Landolt (CSEM SA, Neuchatel, Switzerland) tours the place encoding of numbers by their location in a spot of cellular activity: the concepts, circuits, some perspectives on and extensions of the concepts to difficult computational problems. The theoretical portion defines the relationships between an activity spot on the map and the value it represents, and between two or more maps created by a synthesis of circuits. The validity of the concept for engineers is supported by examples of working VLSI chips. Annotation copyrighted by Book News, Inc., Portland, OR
Place Coding in Analog VLSI
Author: Oliver Landolt
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Neurobiology research suggests that information can be represented by the location of an activity spot in a population of cells (`place coding'), and that this information can be processed by means of networks of interconnections. Place Coding in Analog VLSI defines a representation convention of similar flavor intended for analog-integrated circuit design. It investigates its properties and suggests ways to build circuits on the basis of this coding scheme. In this electronic version of place coding, numbers are represented by the state of an array of nodes called a map, and computation is carried out by a network of links. In the simplest case, a link is just a wire connecting a node of an input map to a node of an output map. In other cases, a link is an elementary circuit cell. Networks of links are somewhat reminiscent of look-up tables in that they hardwire an arbitrary function of one or several variables. Interestingly, these structures are also related to fuzzy rules, as well as some types of artificial neural networks. The place coding approach provides several substantial benefits over conventional analog design: Networks of links can be synthesized by a simple procedure whatever the function to be computed. Place coding is tolerant to perturbations and noise in current-mode implementations. Tolerance to noise implies that the fundamental power dissipation limits of conventional analog circuits can be overcome by using place coding. The place coding approach is illustrated by three integrated circuits computing non-linear functions of several variables. The simplest one is made up of 80 links and achieves submicrowatt power consumption in continuous operation. The most complex one incorporates about 1800 links for a power consumption of 6 milliwatts, and controls the operation of an active vision system with a moving field of view. Place Coding in Analog VLSI is primarily intended for researchers and practicing engineers involved in analog and digital hardware design (especially bio-inspired circuits). The book is also a valuable reference for researchers and students in neurobiology, neuroscience, robotics, fuzzy logic and fuzzy control.
Analog VLSI Implementation of Neural Systems
Author: Carver Mead
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This volume contains the proceedings of a workshop on Analog Integrated Neural Systems held May 8, 1989, in connection with the International Symposium on Circuits and Systems. The presentations were chosen to encompass the entire range of topics currently under study in this exciting new discipline. Stringent acceptance requirements were placed on contributions: (1) each description was required to include detailed characterization of a working chip, and (2) each design was not to have been published previously. In several cases, the status of the project was not known until a few weeks before the meeting date. As a result, some of the most recent innovative work in the field was presented. Because this discipline is evolving rapidly, each project is very much a work in progress. Authors were asked to devote considerable attention to the shortcomings of their designs, as well as to the notable successes they achieved. In this way, other workers can now avoid stumbling into the same traps, and evolution can proceed more rapidly (and less painfully). The chapters in this volume are presented in the same order as the corresponding presentations at the workshop. The first two chapters are concerned with fmding solutions to complex optimization problems under a predefmed set of constraints. The first chapter reports what is, to the best of our knowledge, the first neural-chip design. In each case, the physics of the underlying electronic medium is used to represent a cost function in a natural way, using only nearest-neighbor connectivity.