In the era of future embedded systems the designer is confronted with multi-processor systems both for performance and energy reasons. Exploiting (sub)task-level parallelism is becoming crucial because the instruction-level parallelism alone is insufficient. The challenge is to build compiler tools that support the exploration of the task-level parallelism in the programs. To achieve this goal, we have designed an analysis framework to evaluate the potential parallelism from sequential object-oriented programs. Parallel-performance and data-access analysis are the crucial techniques for estimation of the transformation effects. We have implemented support for platform-independent data-access analysis and profiling of Java programs, which is an extension to our earlier parallel-performance analysis framework. The toolkit comprises automated design-time analysis for performance and data-access characterisation, program instrumentation, program-profiling support and post-processing analysis. We demonstrate the usability of our approach on a number of realistic Java applications.

Complex systems-on-chip present one of the most challenging design problems. To meet this challenge, new design languages capable of modelling such heterogeneous, dynamic systems are needed. For implementation of such a language, the use of an object oriented C++ class library has proven to be a promising approach, since new classes dealing with design- and platform-specific problems can be added in a conceptual and seamlessly reusable way. This paper shows the development of such an extension aimed to provide a platform-independent high-level structured storage object through hiding of the low-level implementation details. It results in a completely virtualised, user-extendible component, suitable for use in heterogeneous systems.

The problem of an efficient hardware implementation of multiplications with one or more constants is encountered in many different digital signal-processing areas, such as image processing or digital filter optimization. In a more general form, this is a problem of common subexpression elimination, and as such it also occurs in compiler optimization and many high-level synthesis tasks. An efficient solution of this problem can yield significant improvements in important design parameters like implementation area or power consumption. In this paper, a new solution of the multiple constant multiplication problem based on the common subexpression elimination technique is presented. The performance of our method is demonstrated primarily on a finite-duration impulse response filter design. The idea is to implement a set of constant multiplications as a set of add-shift operations and to optimize these with respect to the common subexpressions afterwards. We show that the number of add/subtract operations can be reduced significantly this way. The applicability of the presented algorithm to the different high-level synthesis tasks is also indicated. Benchmarks demonstrating the algorithm's efficiency are included as well.