PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System...

PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures

C Giannoula, P Yang, I Fernandez, J Yang… - Proceedings of the …, 2024 - dl.acm.org
Graph Neural Networks (GNNs) are emerging models to analyze graph-structure data. GNN
execution involves both compute-intensive and memory-intensive kernels. The latter kernels …
被引用次数：1 相关文章 所有 2 个版本

Accelerating Graph Neural Networks on Real Processing-In-Memory Systems

C Giannoula, P Yang, I Fernandez Vega… - arXiv e …, 2024 - ui.adsabs.harvard.edu
Abstract Graph Neural Networks (GNNs) are emerging ML models to analyze graph-
structure data. Graph Neural Network (GNN) execution involves both compute-intensive and …
被引用次数：7 相关文章
[PDF] arxiv.org

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator

G Li, S Ye, C Chen, Y Wang, F Yang, T Cao… - arXiv preprint arXiv …, 2025 - arxiv.org
The emergence of neural network capabilities invariably leads to a significant surge in
computational demands due to expanding model sizes and increased computational …
相关文章 所有 3 个版本
[PDF] arxiv.org

Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA

X Zhu, H Zhang, JK Lee, J Zhu, C Pal, S Saha… - arXiv preprint arXiv …, 2024 - arxiv.org
Modern Neural Network (NN) architectures heavily rely on vast numbers of multiply-
accumulate arithmetic operations, constituting the predominant computational cost …
相关文章 所有 2 个版本