50 citations · 67 across the 11 of their papers we have counts for
13 papers
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…
Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems
Christina Giannoula, Ivan Fernandez, Juan Gómez-Luna +3
Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks a…
SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems
Christina Giannoula, Ivan Fernandez, Juan Gómez-Luna +3
Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks a…
Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-in-Memory Hardware
Juan Gómez-Luna, Izzat El Hajj, Ivan Fernandez +3
Many modern workloads such as neural network inference and graph processing are fundamentally memory-bound. For such workloads, data movement between memory and CPU cores imposes a…
Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture
Juan Gómez-Luna, Izzat El Hajj, Ivan Fernandez +3
Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU c…
DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks
Geraldo F. Oliveira, Juan Gómez-Luna, Lois Orosa +5
Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ…
SynCron: Efficient Synchronization Support for Near-Data-Processing Architectures
Christina Giannoula, Nandita Vijaykumar, Nikela Papadopoulou +7
Near-Data-Processing (NDP) architectures present a promising way to alleviate data movement costs and can provide significant performance and energy benefits to parallel applicatio…
NATSA: A Near-Data Processing Accelerator for 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…