High-speed CAM-based architecture for a Prolog machine (ASCA)

Naganuma, J.; Ogura, Takeshi; Yamada, S.; Kimura, Toshiya

doi:10.1109/12.8703

Cited by 25 publications

(4 citation statements)

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“…This is because associative pattern matching and recall is central to pattern-directed processing which is at the heart of many problem solving paradigms in AI (e.g., knowledge-based expert systems, case based reasoning) as well as computer science (e.g., database query processing, in-formation retrieval). As a result, design, VLSI implementation, and applications of associative processors have been studied extensively in the literature [9], [10], [30], [38], [41], [45], [53], [62], [63], [77], [79]. Conventional computer systems employ inherently sequential index or matrix structure for the purpose of table lookup which entails a process pattern matching.…”

Section: Summary and Discussionmentioning

confidence: 99%

A neural-network architecture for syntax analysis

Chen

Honavar

1999

IEEE Trans. Neural Netw.

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Abstract-Artificial neural networks (ANN's), due to their inherent parallelism, offer an attractive paradigm for implementation of symbol processing systems for applications in computer science and artificial intelligence. This paper explores systematic synthesis of modular neural-network architectures for syntax analysis using a prespecified grammar-a prototypical symbol processing task which finds applications in programming language interpretation, syntax analysis of symbolic expressions, and high-performance compilers. The proposed architecture is assembled from ANN components for lexical analysis, stack, parsing and parse tree construction. Each of these modules takes advantage of parallel content-based pattern matching using a neural associative memory. The proposed neural-network architecture for syntax analysis provides a relatively efficient and high performance alternative to current computer systems for applications that involve parsing of LR grammars which constitute a widely used subset of deterministic context-free grammars. Comparison of quantitatively estimated performance of such a system [implemented using current CMOS very large scale integration (VLSI) technology] with that of conventional computers demonstrates the benefits of massively parallel neuralnetwork architectures for symbol processing applications.Index Terms-Lexical analysis, modular neural networks, neural associative processing, neural associative processor, neural parser, neural symbolic processing, parsing, syntax analysis.

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Section: Summary and Discussionmentioning

confidence: 99%

A neural-network architecture for syntax analysis

Chen

Honavar

1999

IEEE Trans. Neural Netw.

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“…Associative memories are also being used in the design of the prolog machines for efficient handling of backtracking and unification operations [14,15,20]. Nahanuma et al, [14] show how a CAM with a maskable equality search, maskable write, and the garbage collection abilities discussed in [15] can reduce the backtracking time to a small, constant value regardless of the number of bindings. Stormon et al, [20] also show how a CAM can be used to speed performance on unification through clause filtering.…”

Section: Associative Memory Applicationsmentioning

confidence: 99%

Modular Scheme for Designing Special PurposeAssociative Memories and Beyond

Hurson

Pakzad

1993

VLSI Design

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The use of associative memories—storage devices that allow data retrieval based on contents—has often been suggested to speed up the performance of many applications. Until recently, using such content-addressable memories (CAMs) was unfeasible due to their high hardware cost. However, the advent of VLSI has made the class of fully-parallel associative memory cost-effective for implementation. This paper briefly overviews design of several fully parallel associative memories proposed in the literature, concentrating on the design of fully-parallel θ-search CAMs.Existing market realities require that product development be fast and predictable. As a result, design flexibility and automation are becoming increasingly important design features. Using the various CAM designs reviewed, the paper collects the features of these designs into a general, modular CAM organization and describes its major components. The modular CAM organization can be used to design application specific CAMs of varying degrees of functionality. Design and space complexity of a sample associative memory suitable for relational database operations is studied. Finally, the application of genetic algorithms as a means to developing automated design tools for fabrication of modular VLSI design chips is discussed.Given a library of CAM modules, the desired functionality and a set of speed and area constraints, this optimization technique produces a suitable CAM design. The proposed technique has been implemented and its performance measure is briefly addressed.

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“…This is because associative pattern matching and recaJl is central to pattern-directed processing which is at the heart of many problem solving paradigms in AI (e.g., knowledge based expert systems, case based reasoning) as well as computer science (e.g., database query processing, information retrieval). As a result, design, VLSI implementation, and applications of associative processors have been studied extensively in the literature [21,23,68,78,88,97,110,124,127,151,153]. The neural network architecture proposed in this chapter for syntax analysis demonstrates the versatility of neural associative processors (memories) as generic building blocks for systematic synthesis of modular massively parallel architectures for symbol processing applications.…”

Section: Summary and Discussionmentioning

confidence: 99%

Neural architectures for database query processing, syntax analysis, knowledge representation, and inference

Chen

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This is to certify that the Doctoral dissertation of Chun-Hsien Chen has met the dissertation requirements of Iowa State University Comm ^ ^mpittee Member Committee Member Committee Member Major Professor College Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. Signature was redacted for privacy. iii

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High-speed CAM-based architecture for a Prolog machine (ASCA)

Cited by 25 publications

References 10 publications

A neural-network architecture for syntax analysis

A neural-network architecture for syntax analysis

Modular Scheme for Designing Special PurposeAssociative Memories and Beyond

Neural architectures for database query processing, syntax analysis, knowledge representation, and inference

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