The architecture for a hardware immune system

Since the advent of fault tolerance in the 1960s, numerous techniques have been developed to increase the reliability of safety critical and space borne missions. In the last decade novel approaches to this field have sought inspiration from nature in the form of evolutionary and developmental forms of fault tolerance. In nature an additional inspiration axis exists in the form of learning. The body's own immune system uses a form of learning to maintain reliable operation in the body even in the presence of invaders. This has only recently been applied as a computational technique in the form of artificial immune systems (AIS). This paper demonstrates a new application of AIS with an immunologically inspired approach to fault tolerance. It is shown a finite state machine can be provided with a hardware immune system to provide a novel form of fault detection giving the ability to detect every faulty state during a normal operating cycle. We call this immunotronics.

[1]  Yoshiki Uchikawa,et al.  Immunoid: An Immunological Approach to Decentralized Behavoir Arbitration of Autonomous Mobile Robots , 1996, PPSN.

[2]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[3]  Andrew M. Tyrrell,et al.  Embryonics+immunotronics: a bio-inspired approach to fault tolerance , 2000, Proceedings. The Second NASA/DoD Workshop on Evolvable Hardware.

[4]  Andrew M. Tyrrell,et al.  Immunotronics: Hardware Fault Tolerance Inspired by the Immune System , 2000, ICES.

[5]  Andrew M. Tyrrell,et al.  MUXTREE Revisited: Embryonics as a Reconfiguration Strategy in Fault-Tolerant Processor Arrays , 1998, ICES.

[6]  G. Habicht,et al.  Immunity and the invertebrates. , 1996, Scientific American.

[7]  Jeffrey O. Kephart,et al.  A biologically inspired immune system for computers , 1994 .

[8]  Tom Routen,et al.  Associative Memory in an Immune-Based System , 1994, AAAI.

[9]  A. Perelson,et al.  Predicting the size of the T-cell receptor and antibody combining region from consideration of efficient self-nonself discrimination. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Adrian Thompson,et al.  Evolutionary techniques for fault tolerance , 1996 .

[11]  Stephanie Forrest,et al.  A sense of self for Unix processes , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[12]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[13]  Cesar Ortega,et al.  MUXTREE Revisited: Embryonics as a Reconfiguration Strategy in Fault-Tolerant Processor Arrays , 1998 .

[14]  石黒 章夫 Fault Diagnosis of Plant Systems Using Immune Networks , 1994 .

[15]  M. Sipper,et al.  Toward robust integrated circuits: The embryonics approach , 2000, Proceedings of the IEEE.

[16]  Essential Immunology , 1981 .

[17]  Alan S. Perelson,et al.  Probability of Self-Nonself Discrimination , 1992 .

[18]  D. Dasgupta,et al.  Immunity-based systems: a survey , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[19]  Spyros Xanthakis,et al.  Immune System and Fault-Tolerant Computing , 1995, Artificial Evolution.

[20]  A. Avizienis,et al.  Fault-tolerance: The survival attribute of digital systems , 1978, Proceedings of the IEEE.

[21]  Patrik D'haeseleer,et al.  An immunological approach to change detection: theoretical results , 1996, Proceedings 9th IEEE Computer Security Foundations Workshop.

[22]  Algirdas Avizienis,et al.  Toward Systematic Design of Fault-Tolerant Systems , 1997, Computer.