Real-world applications of analog and digital evolvable hardware
- 1 January 1999
- journal article
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 3 (3) , 220-235
- https://doi.org/10.1109/4235.788492
Abstract
In contrast to conventional hardware where the structure is irreversibly fixed in the design process, evolvable hardware (EHW) is designed to adapt to changes in task requirements or changes in the environment, through its ability to reconfigure its own hardware structure dynamically and autonomously. This capacity for adaptation, achieved by employing efficient search algorithms based on the metaphor of evolution, has great potential for the development of innovative industrial applications. This paper introduces EHW chips and sis applications currently being developed as part of MITI's Real-World Computing Project; an analog EHW chip for cellular phones, a clock-timing architecture for Giga hertz systems, a neural network EHW chip capable of autonomous reconfiguration, a data compression EHW chip for electrophotographic printers, and a gate-level EHW chip for use in prosthetic hands and robot navigation.Keywords
This publication has 16 references indexed in Scilit:
- The GRD chip: genetic reconfiguration of DSPs for neural network processingIEEE Transactions on Computers, 1999
- Promises and challenges of evolvable hardwareIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 1999
- Online Evolution for a Self-Adapting Robotic Navigation System Using Evolvable HardwareArtificial Life, 1998
- Comparison of evolutionary methods for smoother evolutionPublished by Springer Nature ,1998
- A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systemsIEEE Transactions on Evolutionary Computation, 1997
- Evolution of homing navigation in a real mobile robotIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996
- Comparative Bibliography of Ontogenic Neural NetworksPublished by Springer Nature ,1994
- Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and TeachingMachine Learning, 1992
- WISARD·a radical step forward in image recognitionSensor Review, 1984
- Adaptation Algorithms for Binary Tree NetworksIEEE Transactions on Systems, Man, and Cybernetics, 1979