This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested. INTRODUCTION Data compression is often referred to as coding, where coding is a very general term encompassing any special representation of data which satisfies a given need. Information theory is defined to be the study of eff...

We address the problem of code size minimization in VLSI systems with embedded DSP processors. Reducing code size reduces the production cost of embedded systems. We use data compression methods to develop code size minimization strategies. We present a framework for code size minimization where the compressed data consists of a dictionary and a skeleton. The dictionary can be computed using popular text compression algorithms. We describe two methods to execute the compressed code that have varying performance characteristics and varying degrees of freedom in compressing the code. Experimental results obtained with a TMS320C25 code generator are presented. 1: Introduction An increasingly common micro-architecture for embedded systems is to integrate a microprocessor or microcontroller, a ROM and an ASIC all on a single integrated circuit (Figure 1). Such a micro-architecture can currently be found in such diverse embedded systems as FAX modems, laser printers and cellular telephones....

The main contribution of this thesis is a study of the dynamic programming and greedy strategies for solving combinatorial optimization problems. The study is carried out in the context of a calculus of relations, and generalises previous work by using a loop operator in the imperative programming style for generating feasible solutions, rather than the fold and unfold operators of the functional programming style. The relationship between fold operators and loop operators is explored, and it is shown how to convert from the former to the latter. This fresh approach provides additional insights into the relationship between dynamic programming and greedy algorithms, and helps to unify previously distinct approaches to solving combinatorial optimization problems. Some of the solutions discovered are new and solve problems which had previously proved difficult. The material is illustrated with a selection of problems and solutions that is a mixture of old and new. Another contribution is the invention of a new calculus, called the graph calculus, which is a useful tool for reasoning in the relational calculus and other non-relational calculi. The graph

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Keyword based search engines are the basic building block of text retrieval systems. Higher level systems like content sensitive search engines and knowledgebased systems still rely on keyword search as the underlying text retrieval mechanism. With the explosive growth in content, Internet and Intranet information repositories require efficient mechanisms to store as well as index data. In this paper we discuss the implementation of the Shrink and Search Engine (SASE) framework which unites text compression and indexing to maximize keyword search performance while reducing storage cost. SASE features the novel capability of being able to directly search through compressed text without explicit decompression. The implementation includes a search server architecture, which can be accessed from a Java front-end to perform keyword search on the Internet. The performance results show that the compression efficiency of SASE is within 7-17% of GZIP one of the best lossless compression scheme...

The advent of deep submicron processing technology has made it possible and desirable to integrate a processor core, a program ROM, and application-specific circuitry all on a single IC. As the complexity of embedded software grows, highlevel languages such as C and C++ are increasingly employed in writing embedded software. Consequently, high-level language compilers have become an essential tool in the development of embedded systems. Fixed-point digital signal processors are among the most commonly embedded cores, due to their favorable performance--cost characteristics. However, these architectures are usually designed and optimized for their application domain, and pose challenges for compiler technology. Traditional compiler optimizations, though necessary, are insufficient for generating efficient and compact code. Therefore, new optimizations are required to produce code of the highest quality in a reasonable amount of time. In this thesis the author presents techniques for co...

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