IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An exact algorithm for the maximal sharing of partial terms in multiple constant multiplications
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Exploiting general coefficient representation for the optimal sharing of partial products in MCMs
SBCCI '06 Proceedings of the 19th annual symposium on Integrated circuits and systems design
Multiplierless multiple constant multiplication
ACM Transactions on Algorithms (TALG)
A new algorithm for elimination of common subexpressions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Design of low complexity digital FIR filters
Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes
Optimization of area under a delay constraint in multiple constant multiplications
ICC'09 Proceedings of the 13th WSEAS international conference on Circuits
Search algorithms for the multiple constant multiplications problem: Exact and approximate
Microprocessors & Microsystems
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Multiple constant multiplications (MCM) problem that is to obtain the minimum number of addition/subtraction operations required to implement the constant multiplications finds itself and its variants in many applications, such as finite impulse response (FIR) filters, linear signal transforms, and computer arithmetic. There have been a number of efficient algorithms proposed for the MCM problem. However, due to the NP-hardness of the problem, the proposed algorithms have been heuristics and cannot guarantee the minimum solution. In this paper, we introduce an approximate algorithm that can ensure the minimum solution on more instances than the previously proposed heuristics and can be extended to an exact algorithm using an exhaustive search. The approximate algorithm has been applied on a comprehensive set of instances including FIR filter and randomly generated hard instances, and compared with the previously proposed efficient heuristics. It is observed from the experimental results that the proposed approximate algorithm finds competitive and better results than the prominent heuristics.