The Design of Inherently Fault-Tolerant Systems

With very large scale integrated (VLSI) circuits having as many as 10 million circuit components on a silicon chip and the potential for an order of magnitude more when considering wafer scale integration (WSI), two questions arise. First, how can all these components be organized so that a useful function is performed? For a device such as a random access memory, the organization is relatively obvious. However, if processing elements are considered, the organization is significantly complicated and may change, depending upon the application. Second, how can all these circuit components work together reliably? One approach, known as fault avoidance, is to manufacture the parts using very rigorous and expensive procedures and to take into consideration all possible environmental influences when designing, fabricating, and packaging the device. A second approach is to employ fault-tolerant design techniques.

[1]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[2]  K. Leibovic Information Processing in The Nervous System , 1969, Springer Berlin Heidelberg.

[3]  John K. Ousterhout,et al.  A Switchbox Router with Obstacle Avoidance , 1984, 21st Design Automation Conference Proceedings.

[4]  Barry W. Johnson Design & analysis of fault tolerant digital systems , 1988 .

[5]  I A Meinertzhagen,et al.  The accuracy of the patterns of connexions of the first- and second-order neurons of the visual system of Calliphora , 1970, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  E. Harth,et al.  The escape of Tritonia: dynamics of a neuromuscular control mechanism. , 1975, Journal of theoretical biology.

[7]  Ernest C. Pollard,et al.  Aspects of Biophysics , 1979 .

[8]  D. Hofstadter,et al.  Godel, Escher, Bach: An Eternal Golden Braid , 1979 .

[9]  Lawrence D. Jackel,et al.  Artificial neural networks for computing , 1986 .

[10]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[11]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[12]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Demetri Psaltis,et al.  Optical Neural Computers , 1987, Topical Meeting on Optical Computing.