7 papers
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…
A comprehensive evaluation of spatial co-execution on GPUs using MPS and MIG technologies
Jorge Villarrubia, Luis Costero, Francisco D. Igual +1
To mitigate the increasingly common underutilization of computational resources in modern GPUs, spatial sharing methods enable multiple applications to use them simultaneously. Thi…
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…
Teaching Experiences using the RVfpga Package
D. Chaver, S. Harris, L. Pinuel +13
The RVfpga course offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-w…
Leveraging Multi-Instance GPUs through moldable task scheduling
Jorge Villarrubia, Luis Costero, Francisco D. Igual +1
NVIDIA MIG (Multi-Instance GPU) allows partitioning a physical GPU into multiple logical instances with fully-isolated resources, which can be dynamically reconfigured. This work h…
Balanced segmentation of CNNs for multi-TPU inference
Jorge Villarrubia, Luis Costero, Francisco D. Igual +1
In this paper, we propose different alternatives for convolutional neural networks (CNNs) segmentation, addressing inference processes on computing architectures composed by multip…