Zobrazeno 1 - 10
of 275
pro vyhledávání: '"Near-data processing"'
Autor:
Hyungkyu Ham, Hyunuk Cho, Minjae Kim, Jueon Park, Jeongmin Hong, Hyojin Sung, Eunhyeok Park, Euicheol Lim, Gwangsun Kim
Publikováno v:
IEEE Access, Vol 12, Pp 142651-142667 (2024)
Currently, GPUs face significant challenges due to limited off-chip bandwidth (BW) and memory capacity during DNN training. To address these bottlenecks, we propose a memory access-triggered near-data processing matNDP architecture that offloads memo
Externí odkaz:
https://doaj.org/article/5e0733ab887241b48181918dc53d2a04
Publikováno v:
IEEE Access, Vol 12, Pp 10349-10365 (2024)
The accurate simulation and performance assessment of Near-Data Accelerators (NDAccs) is a complex challenge as it must consider the operation of the entire processing system, the impact of the Operating System (OS) overheads, and the memory contenti
Externí odkaz:
https://doaj.org/article/1cf84f4545ba491394df8ba05e60691d
Autor:
Alain Denzler, Geraldo F. Oliveira, Nastaran Hajinazar, Rahul Bera, Gagandeep Singh, Juan Gomez-Luna, Onur Mutlu
Publikováno v:
IEEE Access, Vol 11, Pp 22136-22154 (2023)
Stencil computations are commonly used in a wide variety of scientific applications, ranging from large-scale weather prediction to solving partial differential equations. Stencil computations are characterized by three properties: 1) low arithmetic
Externí odkaz:
https://doaj.org/article/738ad07eb8804f98b2926bdb39302bd3
Publikováno v:
Memories - Materials, Devices, Circuits and Systems, Vol 4, Iss , Pp 100051- (2023)
The von Neumann bottleneck is imposed due to the explosion of data transfers and emerging data-intensive applications in heterogeneous system architectures. The conventional computation approach of transferring data to CPU is no longer suitable espec
Externí odkaz:
https://doaj.org/article/744f630507f1480b8ee928274bc70bf3
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Access, Vol 10, Pp 46796-46807 (2022)
Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in t
Externí odkaz:
https://doaj.org/article/1ee3a195f8264bd29bba085e5b70c04a
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 4, Iss 1, Pp 66-102 (2022)
Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption become key bottlenecks in t
Externí odkaz:
https://doaj.org/article/f81356e16834462ebc62a7aee654221e
Autor:
Juan Gomez-Luna, Izzat El Hajj, Ivan Fernandez, Christina Giannoula, Geraldo F. Oliveira, Onur Mutlu
Publikováno v:
IEEE Access, Vol 10, Pp 52565-52608 (2022)
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 cores imposes a significant overhead in terms of both latency and energ
Externí odkaz:
https://doaj.org/article/b1cd811d100247a0b6fceb09ee3fae8c
Akademický článek
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