Abstract. The coarse-grained reconfigurable architectures have advantages over the traditional FPGAs in terms of delay, area and configuration time. To execute entire applications, most of them combine an instruction set processor (ISP) and a reconfigurable matrix. However, not much attention is paid to the integration of these two parts, which results in high communication overhead and programming difficulty. To address this problem, we propose a novel architecture with tightly coupled very long instruction word (VLIW) processor and coarse-grained reconfigurable matrix. The advantages include simplified programming model, shared resource costs, and reduced communication overhead. To exploit this architecture, our previously developed compiler framework is adapted to the new architecture. The results show that the new architecture has good performance and is very compiler-friendly.

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 highlevel 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 highlevel synthesis tasks is also indicated. Benchmarks demonstrating the algorithm's efficiency are included as well.

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