6 citations · 12 across the 8 of their papers we have counts for
13 papers · 1 filter
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…
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…
SpChar: Characterizing the Sparse Puzzle via Decision Trees
Francesco Sgherzi, Marco Siracusa, Ivan Fernandez +2
Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommender systems. The performance of sparse kernel…
ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems
Nika Mansouri Ghiasi, Nandita Vijaykumar, Geraldo F. Oliveira +7
Partitioning applications between NDP and host CPU cores causes inter-segment data movement overhead, which is caused by moving data generated from one segment (e.g., instructions,…
Accelerating Time Series Analysis via Processing using Non-Volatile Memories
Ivan Fernandez, Christina Giannoula, Aditya Manglik +6
Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence…
Exploiting Near-Data Processing to Accelerate Time Series Analysis
Ivan Fernandez, Ricardo Quislant, Christina Giannoula +5
Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and m…