3 citations · 5 across the 3 of their papers we have counts for
3 papers
ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System
Marzieh Barkhordar, Alireza Tabatabaeian, Mohammad Sadrosadati +5
Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face…
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
Gagandeep Singh, Dionysios Diamantopoulos, Juan Gómez-Luna +3
Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and re…
Machine Learning Training on a Real Processing-in-Memory System
Juan Gómez-Luna, Yuxin Guo, Sylvan Brocard +5
Training machine learning algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, proc…