2 papers
cs.AR2026
Evaluating Architectural Trade-offs in CGRAs: The Impact of Scratchpad Memory and Heterogeneity on Compute-Intensive Kernels
MarÃa José Belda, Lara Orlandic, Fernando Castro +3
Modern edge computing applications, particularly high-throughput stream processing like Vision Transformers (ViTs), demand massive spatial parallelism and efficient data movement u…
cs.AR2026
Exploiting pre-optimized kernels with polyhedral transformations for CGRA compilation
Yuxuan Wang, MarÃa José Belda, Fernando Castro +3
Modern computing workloads commonly involve matrix-matrix multiplication (mmul) as a core computing pattern. Coarse-Grained Reconfigurable Arrays (CGRAs) can flexibly and efficient…