A hierarchical clustering method for the multiple constant multiplication problem

In this paper, we propose an efficient solution for the Multiple Constant Multiplication (MCM) problem. The method uses hierarchical clustering to exploit common subexpressions among constants and reduces the number of shifts, additions, and subtractions. The algorithm defines appropriate weights, which indicate operation priority, and selects common subexpressions, resulting in a minimum number of local operations. It can also be extended to various high-level synthesis tasks such as arbitrary linear transforms. Experimental results for several error-correcting codes, digital filters and Discrete Cosine Transforms (DCTs) have shown the effectiveness of our method.