Using Hopfield-type neural network model, we present an algorithm for reconstructing 3D mesh processor arrays using single-track switches where spare processors are laid on the six surfaces of a 3D array and show its effectiveness in terms of reconstruction rate and computing time by computer simulation. Next, we show how the algorithm can be realized by a digital neural circuit. It consists of subcircuits for finding candidate compensation paths, deciding whether the neural system reaches a stable state and at the time the system energy is minimum, and subcircuits for neurons. The subcircuit for each neuron including the other subcircuits can only be made with 16 gates and two flip-flops. Since the state transitions are done in parallel, the circuit will be able to find a set of compensation paths for a fault pattern very quickly within a time less than 1 µs. Furthermore, the hardware implementation of the algorithm leads to making a self-reconfigurable system without the aid of a host computer.

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 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 reliabilites are achieved while processing time is a little and hardware overhead (links and switches) required for reconstruction is as same as that for the track switch model. Next, we present a hardware implementation of the neural algorithm so that a built-in self-reconfigurable scheme may be realized.