PATTERN RECOGNITION AND INTERPRETATION BY A BINARY-TREE OF PROCESSORS

High-level-language-architecture research is an area which focuses upon narrowing the so-called "semantic gap" between high-level programming languages and computer hardware. The goal is to improve processing efficiency by bringing hardware and software design closer together. This goal is typically achieved by considering software and hardware aspects concurrently as part of an overall design process.;A large numer of multiprocessor organizations exist today. A fundamental problem of multiprocessing is becoming less one of how to physically organize the processor, and more one of how to program it. The difficulty associated with programming multiprocessors is characterized here as a "parallel semantic gap".;The research described within is motivated by the direct interpretation model used to narrow the sequential semantic gap. The direct implementation of an interpreter on some multiprocessor organization is proposed. The specific approach is to study syntax-directed interpretation on a binary-tree multiprocessor organization.;Any interpretation scheme must use some pattern recognition algorithm to discern the actions that programs are to carry out. This dissertation presents two new recognition algorithms for a binary-tree multiprocessor and studies the application of these algorithms to parallel interpretation.;A variety of approaches exist within this area, ranging from machines having optimized instruction sets to direct execution architectures where high-level tokens are fetched from program memory like low-level instructions. A key aspect of any high-level-language architecture is that the execution algorithm can be modeled as language translation. Any high-level-language architecture is effectively a direct implementation of an interpreter.;Language interpretation is not the only application which these algorithms have. Compelling research directions are suggested for architectures supporting expert systems and complex pattern analysis. Included among these are machines for information retrieval from a semantic-network knowledge base and ones which perform scene analysis by detecting graph isomorphisms.