1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AR2023
Accelerating Unstructured SpGEMM using Structured In-situ Computing
Huize Li, Tulika Mitra
Sparse matrix-matrix multiplication (SpGEMM) is a critical kernel widely employed in machine learning and graph algorithms. However, real-world matrices' high sparsity makes SpGEMM…
cs.AR2023
Flip: Data-Centric Edge CGRA Accelerator
Dan Wu, Peng Chen, Thilini Kaushalya Bandara +2
Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statica…
cs.AR2023★ 1 cited
Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs
Dhananjaya Wijerathne, Zhaoying Li, Tulika Mitra
Coarse-Grained Reconfigurable Arrays (CGRAs) hold great promise as power-efficient edge accelerator, offering versatility beyond AI applications. Morpher, an open-source, architect…