Embedded systems are constrained by the available memory. Code-compression techniques address this issue by reducing the code size of application programs. It is a major challenge to develop an efficient code-compression technique that can generate substantial reduction in code size without affecting the overall system performance. We present a novel code-compression technique using bitmasks, which significantly improves the compression efficiency without introducing any decompression penalty. This paper makes three important contributions. 1) It develops an efficient bitmask-selection technique that can create a large set of matching patterns. 2) It develops an efficient dictionary-selection technique based on bitmasks. 3) It proposes a dictionary-based code-compression algorithm using the bitmask- and dictionary-selection techniques that can significantly reduce the memory requirement. To demonstrate the usefulness of our approach, we have performed code compression using applications from various domains and compiled for a wide variety of architectures. Our approach outperforms the existing dictionary-based techniques by an average of 20%, giving a compression ratio of 55%-65%.

Memory plays a crucial role in designing embedded systems. A larger memory can accommodate more and large applications but increases cost, area, as well as energy requirements. Code compression techniques address this problem by reducing the size of the applications. While early work on bitmask-based compression has proposed several promising ideas, many challenges remain in applying them to embedded system design. This paper makes two important contributions to address these challenges by developing application-specific bitmask selection and bitmask-aw are dictionary selection techniques. The authors applied these techniques for code compression of TI and MediaBench applications to demonstrate the usefulness of the approach

Embedded systems are constrained by the available memory. Code compression techniques address this issue by reducing the code size of application programs. Dictionary-based code compression techniques are popular because they offer both good compression ratio and fast decompression scheme. Recently proposed techniques [8, 9] improve standard dictionary-based compression by consideringmismatches. This paper makes two important contributions: i) it provides a cost-benefit analysis framework for improving the compression ratio by creating more matching patterns, and ii) it develops an efficient code compression technique using bitmasks to improve the compression ratio without introducing any decompression penalty. To demonstrate the usefulness of our approach we have used applications from various domains and compiled for a wide variety of architectures. Our approach outperforms the existing dictionary-based techniques by an average of 15%, giving a compression ratio of 55% - 65%.

We discuss Morpheus, a data transformation construction tool and associated repository. The architecture of Morpheus is motivated by the goal to reuse (pieces of) previously written transformations to solve data integration problems by finding relevant ones in the repository and then modifying them for repurposing. In addition, Morpheus is integrated with a DBMS so as to leverage existing capabilities including the runtime environment for transforms. We discuss the architecture of Morpheus and illustrate its usage with the help of a simple transform construction scenario.