Performance Analysis of Matrix Multiplication for Deep Learning on the Edge
<|reference_start|>Performance Analysis of Matrix Multiplication for Deep Learning on the Edge: The devices designed for the Internet-of-Things encompass a large variety of distinct processor architectures, forming a highly heterogeneous zoo. In order to tackle this, we employ a simulator to estimate the performance of the matrix-matrix multiplication (GEMM) kernel on processors designed to operate at the edge. Our simulator adheres to the modern implementations of GEMM, advocated by GotoBLAS2, BLIS, OpenBLAS, etc., to carefully account for the amount of data transfers across the memory hierarchy of different algorithmic variants of the kernel. %Armed with this tool, A small collection of experiments provide the necessary data to calibrate the simulator and deliver highly accurate estimations of the execution time for a given processor architecture.<|reference_end|>
@article{ramírez2024performance,
    title={Performance Analysis of Matrix Multiplication for Deep Learning on the
  Edge},
    author={Cristian Ram'irez, Adri'an Castell'o, H'ector Mart'inez, and
  Enrique S. Quintana-Ort'i},
    journal={High Performance Computing. ISC High Performance 2022
  International Workshops. ISC High Performance 2022. Lecture Notes in Computer
  Science, vol 13387. Springer, Cham},
    year={2024},
    doi={10.1007/978-3-031-23220-6_5},
    archivePrefix={arXiv},
    eprint={2403.07731},
    primaryClass={cs.AR}
}