Scalable and efficient linear algebra kernel mapping for low energy consumption on the layers CGRA

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 64×64 to 16384×16384. 12 architectural variants with up to 10×10 processing elements were used to explore scalability of the mapping and the architecture, achieving < 10% energy increase for architectures up to 8×8 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.