Abstract
Reconfigurable computing architectures promise to substantially increase the performance of computations through the customization of data-path and storage structures best suited to the specific needs of each computation. The need to synthesize, either fully or partially, the structure of the target architecture while simultaneously attempting to optimize the mapping of the computation to that architecture creates a vast design space exploration (DSE) challenge. In this paper we describe current approaches to this DSE problem using program analysis, estimation, modeling and empirical optimization techniques. We also describe a unified approach for this DSE challenge in which these techniques can be complemented with history- and learning-based approaches.
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Diniz, P.C. (2004). Design Space Exploration for Configurable Architectures and the Role of Modeling, High-Level Program Analysis and Learning Techniques. In: Pimentel, A.D., Vassiliadis, S. (eds) Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2004. Lecture Notes in Computer Science, vol 3133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27776-7_23
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DOI: https://doi.org/10.1007/978-3-540-27776-7_23
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