A Built-In Self-Reconstruction Approach for Partitioned Mesh-Arrays Using Neural Algorithm (Special Issue on Architectures, Algorithms and Networks for Massively parallel Computing)

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Various reconfiguration schemes against faults of mesh-connected processor arrays have been proposed. As one of them, the mesh-connected processor arrays model based on single-track switches was proposed in [1]. The model has an advantage of its inherent simplicity of the routing hardware. Furthermore, the 2 1/2 track switch model [2] and the multiple track switch model [3] were proposed to enhance yields and reliabilities of arrays. However, in these models, Simplicity of the routing hardware is somewhat lost because multiple tracks are used for each row and column. In this paper, we present a builtin self-reconstruction approach for mesh-connected processor arrays which are partitioned into sub-arrays each using single-track switches. Spare PEs which are located on the boundaries of the sub-arrays compensate faulty PEs in these sub-arrays. First, we formulate a reconfigulation algorithm for partitioned mesh-arrays using a Hopfield-type neural network, and then its performance for reconfigulation in terms of survival rates and reliabilities of arrays and processing time are investigated by computer simulations. From the results, we can see that high reliabilities are achieved while processing time is a little and hardware overhead (links and switches) required for reconstruction is as same as that for the 1 1/2 track switch model. Next, we present a hardware implementation of the neural algorithm so that a built-in self-reconfigurable scheme may be realized.
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1996-08-25

A Built-In Self-Reconstruction Approach for Partitioned Mesh-Arrays Using Neural Algorithm (Special Issue on Architectures, Algorithms and Networks for Massively parallel Computing)

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