Abstract
A scalable mapping is proposed for 3 important kernels from the Numerical Linear Algebra domain, to exploit architectural features to reach asymptotically optimal efficiency and a low energy consumption. Performance and power evaluations were done with input data set matrix sizes ranging from 64\(\times \)64 to 16384\(\times \)16384. 12 architectural variants with up to 10\(\times \)10 processing elements were used to explore scalability of the mapping and the architecture, achieving \(<10\,\%\) energy increase for architectures up to 8\(\times \)8 PEs coupled with performance speed-ups of more than an order of magnitude. This enables a clean area-performance trade-off on the Layers architecture while keeping energy constant over the variants.
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Rákossy, Z.E., Stengele, D., Acosta-Aponte, A., Chafekar, S., Bientinesi, P., Chattopadhyay, A. (2015). Scalable and Efficient Linear Algebra Kernel Mapping for Low Energy Consumption on the Layers CGRA. In: Sano, K., Soudris, D., Hübner, M., Diniz, P. (eds) Applied Reconfigurable Computing. ARC 2015. Lecture Notes in Computer Science(), vol 9040. Springer, Cham. https://doi.org/10.1007/978-3-319-16214-0_25
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DOI: https://doi.org/10.1007/978-3-319-16214-0_25
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