29 citations · 44 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses
Rakesh Nadig, Mohammad Sadrosadati, Haiyu Mao +6
The performance and capacity of solid-state drives (SSDs) are continuously improving to meet the increasing demands of modern data-intensive applications. Unfortunately, communicat…
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,…
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Can Firtina, Kamlesh Pillai, Gurpreet S. Kalsi +10
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequen…
PiDRAM: An FPGA-based Framework for End-to-end Evaluation of Processing-in-DRAM Techniques
Ataberk Olgun, Juan Gomez Luna, Konstantinos Kanellopoulos +4
DRAM-based main memory is used in nearly all computing systems as a major component. One way of overcoming the main memory bottleneck is to move computation near memory, a paradigm…
PiDRAM: A Holistic End-to-end FPGA-based Framework for Processing-in-DRAM
Ataberk Olgun, Juan Gómez Luna, Konstantinos Kanellopoulos +4
Processing-using-memory (PuM) techniques leverage the analog operation of memory cells to perform computation. Several recent works have demonstrated PuM techniques in off-the-shel…
SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems
Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwasniewski +15
Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such a…