Invariance of complexity measures for networks with unreliable gates
Journal of the ACM (JACM)
Symmetry in self-correcting cellular automata
Journal of Computer and System Sciences
Information theory and noisy computation
Information theory and noisy computation
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Reliable computation by networks in the presence of noise
IEEE Transactions on Information Theory
Signal propagation and noisy circuits
IEEE Transactions on Information Theory
MCA model for simulating the failure of microinhomogeneous materials
Journal of Nanomaterials
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A commonly used model for fault-tolerant computation is that of cellular automata. The essential difficulty of fault-tolerant computation is present in the special case of simply remembering a bit in the presence of faults, and that is the case we treat in this paper. We are concerned with the degree (the number of neighboring cells on which the state transition function depends) needed to achieve fault tolerance when the fault rate is high (nearly 1/2). We consider both the traditional transient fault model (where faults occur independently in time and space) and a recently introduced combined fault model which also includes manufacturing faults (which occur independently in space, but which affect cells for all time). We also consider both a purely probabilistic fault model (in which the states of cells are perturbed at exactly the fault rate) and an adversarial model (in which the occurrence of a fault gives control of the state to an omniscient adversary). We show that there are cellular automata that can tolerate a fault rate 1/2-@x (with @x0) with degree O((1/@x^2)log(1/@x)), even with adversarial combined faults. The simplest such automata are based on infinite regular trees, but our results also apply to other structures (such as hyperbolic tessellations) that contain infinite regular trees. We also obtain a lower bound of @W(1/@x^2), even with only purely probabilistic transient faults.