In this article, we present a nanoscale reconfigurable mesh which is interconnected by ferromagnetic spin-wave buses. In this architecture, unlike the traditional spin-based nano structures which transmit charge, waves are transmitted. As a result, the power consumption of the proposed modules can be low. This reconfigurable mesh, while requiring the same number of switches and buses as the standard reconfigurable mesh, is capable of simultaneously transmitting N waves on each of the spin-wave buses. Because of this highly parallel feature, very fast and fault-tolerant algorithms can be designed. To illustrate the superior performance of the proposed spin-wave reconfigurable mesh, we present three fast labeling algorithms.

Abstract The paper presents a generic technique for mapping parallel algorithms onto parallel architectures. The proposed technique is a fast recursive mapping algorithm which is a component of the Cluster‐M programming tool. The other components of Cluster‐M are the Specification module and the Representation module. In the Specification module, for a given task specified by a high‐level machine‐independent program, a clustered task graph called Spec graph is generated. In the Representation module, for a given architecture or computing organization, a clustered system graph called Rep graph is generated. Given a task (or system) graph, a Spec (or Rep) graph can be generated using one of the clustering algorithms presented in the paper. The clustering is done only once for a given task graph (system graph) independent of any system graphs (task graphs). It is a machine‐independent (application‐independent) clustering, and therefore it is not repeated for different mappings. The Cluster‐M mapping algorithm presented produces a sub‐optimal matching of a given Spec graph containing M task modules, onto a Rep graph of N processors, in O(MN) time. This generic algorithm is suitable for both the allocation problem and the scheduling problem. Its performance is compared to other leading techniques. We show that Cluster‐M produces better or similar results in significantly less time and using fewer or an equal number of processors as compared to the other known methods.

The author studies a class of VLSI organizations with optical interconnects for fast solutions to several image processing tasks. The organization and operation of these architectures are based on a generic model called OMC, which is used to understand the computational limits in using free space optics in VLSI parallel processing systems. The relationships between OMC and shared memory models are discussed. Also, three physical implementations of OMC are presented. Using OMC, several parallel algorithms for fine grain image computing are presented. A set of processor efficient optimal O(log N) algorithms and a set of constant time algorithms are presented for finding geometric properties of digitized images. Finally, designs tailored to meet both the computation and communication needs of problems such as those involving irregular sparse matrices are examined.>

An efficient parallel processing algorithm for detecting straight lines on a mesh-connected computer enhanced with a gate-connection network (GCN) is presented. The algorithm is composed of a modified Hough transform that projects compressed pixels in parallel in a given direction and a parallel procedure that extracts the beginning and end points of detected lines. Both parts require the flexible communication capabilities of the enhance mesh. The GCN can be used to dynamically reconfigure the interconnections between hundreds of processors. It is shown how the GCN can electrically connect all of the edge pixels on a straight line. For an n*n pixel array, the algorithm can detect all lines in O(log n) time. Initial experimental results obtained using a simulator of the GCN implemented on a very-large-scale integration (VLSI) chip are presented. Though the accuracy of the algorithm depends largely on the assigned threshold values, the authors believe its speed is superior to that of any other Hough-based technique by a factor of at least two orders of magnitude.>