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The output of high-level synthesis typically consists of a netlist of generic RTL components and a state sequencing table. vVhile module generators and logic synthesis tools can be used to map RTL components into standard cells or layout geometries, they cannot provide technology mapping into the data book libraries of functional RTL cells used commonly throughout the industrial design community. In this paper, we introduce an approach to implementing generic RTL components with technology-specific RTL library cells. This approach addresses the criticism of designers who feel that high-level synthesis tools should be used in conjunction with existing RTL data books. vVe describe how GENUS, a library of generic RTL components, is organized for use. in high-level synthesis and how DTAS, a functional synthesis system, is used to map GENUS components into RTL library cells.

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Irvine, C A 92717 A B S T R A C TThe goal of logic synthesis is to obtain high-quality designs from specifications. Current approaches to logic synthesis often tradeoff design quality for technology independence. In this paper, we present a model of logic synthesis that uses technology-specific design rules and extends rule-based search to functional decomposition and technology mapping. While this model improves design quality by taking advantage of the target technology, it is not robust to technology changes. To improve robustness, we augment the model with two learning components: one for acquiring rules that make use of physical cells in a technology library, and another for acquiring rules that make use of appropriate design styles. These components are related to work in learning of macrooperators and explanation-based learning.

A side effect of AI research is the development of programming languages as vehicles for experimentation and demonstration of concepts. These AI languages generally require some form of parser front end, which can be nontrivial to build. Parser generators can ease the task of language development, but commonly available generators use parsing technologies that severely constrain the level of syntactic sophistication, such as allowing at most one symbol of look ahead. Further, these generators are most often targeted for C and Ada applications; parser generators for LISP applications are not widely available. The RAND Advanced Compiler Kit (RACK) is a parser generator for generalized LR parsing and is suitable for AI applications. RACK parsers are unique in their ability to recognize non-LR(k) languages, as well as LR(k) languages for k > 1. RACK is implemented in C and is upwardly compatible with YACC, a widely used parser generator for C applications. RACK generates parsers that interface with C or Common Lisp. RACK also includes features such as arbitrary look-ahead, multiple start symbols, a scanner generator, and a grammar interpreter. In this paper, I describe RACK, its parsing technology, and significant features; I also report performance results comparing RACK to YACC.