Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
Processing-in-memory (PIM) has been explored for decades by computer architects, yet it
has never seen the light of day in real-world products due to its high design overheads and …
has never seen the light of day in real-world products due to its high design overheads and …
[HTML][HTML] Accelerating large table scan using processing-in-memory technology
A Baumstark, MA Jibril, KU Sattler - Datenbank-Spektrum, 2023 - Springer
Today's systems are capable of storing large amounts of data in main memory. Particularly,
in-memory DBMSs benefit from this development. However, the processing of data from the …
in-memory DBMSs benefit from this development. However, the processing of data from the …
Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
C Giannoula, P Yang, IF Vega, J Yang, YX Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data.
Graph Neural Network (GNN) execution involves both compute-intensive and memory …
Graph Neural Network (GNN) execution involves both compute-intensive and memory …
Simplepim: A software framework for productive and efficient processing-in-memory
Data movement between memory and processors is a major bottleneck in modern
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …
Evaluating Homomorphic Operations on a Real-World Processing-In-Memory System
H Gupta, M Kabra, J Gómez-Luna… - 2023 IEEE …, 2023 - ieeexplore.ieee.org
Computing on encrypted data is a promising approach to reduce data security and privacy
risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this work …
risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this work …
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward
signals from experience datasets. However, RL training often faces memory limitations …
signals from experience datasets. However, RL training often faces memory limitations …
Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
S Rhyner, H Luo, J Gómez-Luna, M Sadrosadati… - arXiv preprint arXiv …, 2024 - arxiv.org
Machine Learning (ML) training on large-scale datasets is a very expensive and time-
consuming workload. Processor-centric architectures (eg, CPU, GPU) commonly used for …
consuming workload. Processor-centric architectures (eg, CPU, GPU) commonly used for …
PID-Comm: A Fast and Flexible Collective Communication Framework for Commodity Processing-in-DIMM Devices
Recent dual in-line memory modules (DIMMs) are starting to support processing-in-memory
(PIM) by associating their memory banks with processing elements (PEs), allowing …
(PIM) by associating their memory banks with processing elements (PEs), allowing …
Analysis of Data Transfer Bottlenecks in Commercial PIM Systems: A Study with UPMEM-PIM
Due to emerging workloads that require high memory bandwidth, Processing-in-Memory
(PIM) has gained significant attention and led several industrial PIM products to be …
(PIM) has gained significant attention and led several industrial PIM products to be …
PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization
DRAM-based processing-in-memory (DRAM-PIM) has gained commercial prominence in
recent years. However, their integration for deep learning acceleration poses inherent …
recent years. However, their integration for deep learning acceleration poses inherent …