From configurable circuits to bio-inspired systems

Field-programmable gate arrays (FPGAs) are large, fast integrated circuits — that can be modified, or configured, almost at any point by the end user. Within the domain of configurable computing we distinguish between two modes of configurability: static—where the configurable processor’ s configuration string is loaded once at the outset, after which it does not change during execution of the task at hand, and dynamic— where the processor’ s configuration may change at any moment. This chapter describes six applications in the domain of configurable computing, considering both static and dynamic systems, including: SPYDER (a reconfigurable processor development system), RENCO (a reconfigurable network computer), an FPGA-based backpropagation neural network, Firefly (an evolving machine), BioWatch (a self- repairing watch), and FAST (a neural network with a flexible, adaptable-size topology). Moreover, we argue that the rise of configurable computing requires a fundamental change in the engineering curriculum, toward which end we present the LABOMAT board, developed for use by students in hardware design courses. While static configurability mainly aims at attaining the classical computing goal of improving performance, dynamic configurability might bring about an entirely new breed of hardware devices — ones that are able to adapt within dynamic environments.1

[1]  D. Benitez-Diaz,et al.  Learning Algorithm with Gaussian Membership Function for Fuzzy RBF Neural Networks , 1995, IWANN.

[2]  Moshe Sipper,et al.  Evolution of Parallel Cellular Machines: The Cellular Programming Approach , 1997 .

[3]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[4]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[5]  Stanley Habib,et al.  Microprogramming and firmware engineering methods , 1988 .

[6]  Gianluca Tempesti,et al.  Embryonics: a new methodology for designing field-programmable gate arrays with self-repair and self-replicating properties , 1998, IEEE Trans. Very Large Scale Integr. Syst..

[7]  M Sipper,et al.  The evolution of parallel cellular machines: toward evolware. , 1997, Bio Systems.

[8]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[9]  Torsten Lehmann,et al.  Nonlinear backpropagation: doing backpropagation without derivatives of the activation function , 1997, IEEE Trans. Neural Networks.

[10]  E. Fiesler,et al.  Comparative Bibliography of Ontogenic Neural Networks , 1994 .

[11]  Bernd Fritzke,et al.  Unsupervised ontogenic networks , 1997 .

[12]  Marco Tomassini,et al.  Towards Evolvable Hardware: The Evolutionary Engineering Approach , 1996 .

[13]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[14]  E. Berlekamp,et al.  Winning Ways for Your Mathematical Plays , 1983 .

[15]  Veljko Milutinovic.  Book Review: MICROPROGRAMMING AND FIRMWARE ENGINEERING METHODS by Stanley Habib, Editor:, Van Nostrand Reinhold, 1988 , 1989, SIGM.

[16]  Y. Ohta.  Knowledge-based interpretation of outdoor natural color scenes , 1998 .

[17]  Moshe Sipper,et al.  Static and Dynamic Configurable Systems , 1999, IEEE Trans. Computers.

[18]  Rajesh Gupta,et al.  Hardware/software co-design , 1996, Proc. IEEE.

[19]  Adrian Thompson,et al.  An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics , 1996, ICES.

[20]  H. C. Zeidler,et al.  On-chip backpropagation training using parallel stochastic bit streams , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[21]  Zoran Salcic,et al.  Digital Systems Design and Prototyping Using Field Programmable Logic , 1997 .

[22]  Michael John Sebastian Smith,et al.  Application-specific integrated circuits , 1997 .

[23]  C. Iseli,et al.  SPYDER: un processeur reconfigurable réalisé à l'aide de circuits FPGA , 1997 .

[24]  Brad Hutchings,et al.  Density enhancement of a neural network using FPGAs and run-time reconfiguration , 1994, Proceedings of IEEE Workshop on FPGA's for Custom Computing Machines.

[25]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[26]  Howard C. Card,et al.  Parallel Random Number Generation for VLSI Systems Using Cellular Automata , 1989, IEEE Trans. Computers.

[27]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[28]  Marco Tomassini,et al.  A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems , 1997, IEEE Trans. Evol. Comput..

[29]  Adrian Thompson,et al.  Silicon evolution , 1996 .

[30]  Eduardo Sanchez,et al.  A platform for co-design and co-synthesis based on FPGA , 1996, Proceedings Seventh IEEE International Workshop on Rapid System Prototyping. Shortening the Path from Specification to Prototype.

[31]  B. Ramakrishna Rau,et al.  Instruction-level Parallelism , 2001 .

[32]  Hiroshi Yamamoto,et al.  Reduction of required precision bits for back-propagation applied to pattern recognition , 1993, IEEE Trans. Neural Networks.

[33]  A. I. Ethem Alpaydin.  Neural models of incremental supervised and unsupervised learning , 1990 .

[34]  Brad Hutchings,et al.  FPGA-based stochastic neural networks-implementation , 1994, Proceedings of IEEE Workshop on FPGA's for Custom Computing Machines.

[35]  Inman Harvey,et al.  Unconstrained Evolution and Hard Consequences , 1995, Towards Evolvable Hardware.

[36]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[37]  E. Sanchez,et al.  Implementation of neural constructivism with programmable hardware , 1996, 1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report.

[38]  Marco Tomassini,et al.  The firefly machine: online evolware , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[39]  Gary Stix,et al.  Finding Pictures on the Web , 1997 .

[40]  Marco Tomassini,et al.  Online Autonomous Evolware , 1996, ICES.

[41]  Andrés Pérez-Uribe,et al.  FPGA Implementation of an Adaptable-Size Neural Network , 1996, ICANN.

[42]  Stephen Wolfram,et al.  Cellular Automata And Complexity , 1994 .

[43]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[44]  Moshe Sipper,et al.  Evolution of Parallel Cellular Machines , 1997, Lecture Notes in Computer Science.

[45]  A. Perez,et al.  The FAST architecture: a neural network with flexible adaptable-size topology , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[46]  Helge J. Ritter,et al.  Adaptive color segmentation-a comparison of neural and statistical methods , 1997, IEEE Trans. Neural Networks.

[47]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[48]  J. M. Aróstegui.  Vlsi architectures for evolutive neural models , 1995 .

[49]  Howard C. Card,et al.  Cellular automata-based pseudorandom number generators for built-in self-test , 1989, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[50]  Masumi Ishikawa,et al.  Structural learning with forgetting , 1996, Neural Networks.

[51]  Jean Vuillemin,et al.  Introduction to programmable active memories , 1990 .

[52]  Paolo Ienne,et al.  Digital Connectionist Hardware: Current Problems and Future Challenges , 1997, IWANN.

[53]  Marco Tomassini,et al.  Towards Evolvable Hardware , 1996, Lecture Notes in Computer Science.

[54]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .