Analog VLSI signal processing: Why, where, and how?

Analog VLSI signal processing is most effective when precision is not required, and is therefore an ideal solution for the implementation of perception systems. The possibility to choose the physical variable that represents each signal allows all the features of the transistor to be exploited opportunistically to implement very dense time- and amplitude-continuous processing cells. This paper describes a simple model that captures all the essential features of the transistor. This symmetrical model also supports the concept of pseudoconductance which facilitates the implementation of linear networks of transistors. Basic combinations of transistors in the current mirror, the differential pair, and the translinear loop are revisited as support material for the description of a variety of building blocks. These examples illustrate the rich catalogue of linear and nonlinear operators that are available for local and collective analog processing. The difficult problem of analog storage is addressed briefly, as well as various means for implementing the necessary intrachip and interchip communication.

[1]  Jean-Didier Legat,et al.  Analog storage of adjustable synaptic weights , 1992, Defense, Security, and Sensing.

[2]  Eric A. Vittoz,et al.  CMOS Integration of Herault-Jutten Cells for Separation of Sources , 1989, Analog VLSI Implementation of Neural Systems.

[3]  K. Bult,et al.  A class of analog CMOS circuits based on the square-law characteristic of an MOS transistor in saturation , 1987 .

[4]  Carver A. Mead,et al.  A two-dimensional visual tracking array , 1988 .

[5]  M. Lenzlinger,et al.  Fowler‐Nordheim Tunneling into Thermally Grown SiO2 , 1969 .

[6]  Eric A. Vittoz,et al.  Linear networks based on transistors , 1993 .

[7]  B. Hochet,et al.  GENERATION OF LEARNING NEIGHBOURHOOD IN KOHONEN FEATURE MAPS BY MEANS OF SIMPLE NONLINEAR NETWORK , 1991 .

[8]  Eric A. Vittoz,et al.  CMOS selfbiased Euclidean distance computing circuit with high dynamic range , 1992 .

[9]  Oliver Landolt.  An analog CMOS implementation of a Kohonen network with learning capability , 1995 .

[10]  TAKESHI YAMAKAWA,et al.  The Current Mode Fuzzy Logic Integrated Circuits Fabricated by the Standard CMOS Process , 1986, IEEE Transactions on Computers.

[11]  Michel Steyaert,et al.  Analog VLSI implementation of Neural Networks , 1992 .

[12]  E.A. Vittoz,et al.  Precision Compressor Gain Controller in CMOS Technology , 1986, ESSCIRC '86: Twelfth European Solid-State Circuits Conference.

[13]  Rolf Schaumann,et al.  Continuous-Time Filters , 1993 .

[14]  W. Guggenbuhl,et al.  An analog trimming circuit based on a floating-gate device , 1988 .

[15]  E. Vittoz,et al.  Analog Storage of Adjustable Synaptic Weights , 1991 .

[16]  B. Gilbert.  Translinear circuits: a proposed classification , 1975 .

[17]  J. Fellrath,et al.  CMOS analog integrated circuits based on weak inversion operations , 1977 .

[18]  Andreas G. Andreou,et al.  A Contrast Sensitive Silicon Retina with Reciprocal Synapses , 1991, NIPS.

[19]  Eric A. Vittoz,et al.  Dynamic analog techniques , 1994 .

[20]  David L. Standley,et al.  Analog CMOS IC for object position and orientation , 1991, Defense, Security, and Sensing.

[21]  Eric A. Vittoz,et al.  Analog VLSI signal processing: Why, where, and how? , 1994 .

[22]  Misha Anne Mahowald,et al.  VLSI analogs of neuronal visual processing: a synthesis of form and function , 1992 .

[23]  S. Tam,et al.  An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.

[24]  Eric A. Vittoz,et al.  Dynamic Current Mirrors , 1993 .

[25]  O. Landolt,et al.  Efficient analog CMOS implementation of fuzzy rules by direct synthesis of multidimensional fuzzy subspaces , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[26]  John B. Hughes,et al.  Switched current filters , 1990 .

[27]  E. Vittoz.  MOS transistors operated in the lateral bipolar mode and their application in CMOS technology , 1983, IEEE Journal of Solid-State Circuits.

[28]  Eric A. Vittoz.  The Design of High-Performance Analog Circuits on Digital CMOS Chips , 1985 .

[29]  Eric A. Vittoz,et al.  Micropower Techniques , 1994 .

[30]  Carver A. Mead,et al.  Scanners for visualizing activity of analog VLSI circuitry , 1991 .

[31]  H.J. De Man,et al.  Adaptive biasing CMOS amplifiers , 1982, IEEE Journal of Solid-State Circuits.

[32]  Alan F. Murray,et al.  Real-Time Autonomous Robot Navigation Using VLSI Neural Networks , 1990, NIPS.

[33]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[34]  Y. Tsividis.  Operation and modeling of the MOS transistor , 1987 .

[35]  Eric A. Vittoz,et al.  A communication architecture tailored for analog VLSI artificial neural networks: intrinsic performance and limitations , 1994, IEEE Trans. Neural Networks.

[36]  B. Gilbert.  A monolithic 16-channels analog array normalizer , 1984, IEEE Journal of Solid-State Circuits.

[37]  Misha Mahowald,et al.  A silicon model of early visual processing , 1993, Neural Networks.

[38]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[39]  Eric A. Vittoz,et al.  Analog VLSI implementation of neural networks , 1990, IEEE International Symposium on Circuits and Systems.

[40]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .