2 papers
cs.AR2026
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4
High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…
cs.AR2025
PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
Christina Giannoula, Peiming Yang, Ivan Fernandez +7
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kerne…