Towards optimal reconfigurable constant multipliers
Résumé
This paper introduces a novel algorithm for generating run-time reconfigurable single constant multipliers (RSCMs) which are optimal within their model in terms of hardware cost. Optimality is ensured by an exhaustive exploration of the design space mixing constraint programming, depth-first search, and branch-and-prune techniques. The cost model of previous works is also refined. Compared to the state of the art, this approach enables much larger constant sets, and also significantly improves the performance of the resulting architectures. Applications to neural network inference and small floating point multiplication units are evaluated.
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