Zobrazeno 1 - 10
of 3 456
pro vyhledávání: '"Memory bandwidth"'
Akademický článek
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Publikováno v:
IEEE Open Journal of the Solid-State Circuits Society, Vol 2, Pp 276-287 (2022)
This work describes a multiplication and accumulation (MAC) accelerator integrated with a memory interface. The link is designed to take advantage of naturally existing sparsity in a neural network. The link operating at 16 Gb/s achieves 0.1875-pJ/bi
Externí odkaz:
https://doaj.org/article/76ab95479bc14ee496d109e2caf84bf8
Publikováno v:
Array, Vol 15, Iss , Pp 100179- (2022)
The efficient utilization of high-performance computing (HPC) system resources under rigorous electric power budget or I/O workload constraints is among the most important goals set by system operators to deal with the demanding requirements of appli
Externí odkaz:
https://doaj.org/article/42e487089bc8472c8660b1ede0d53361
Akademický článek
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Publikováno v:
Applied Sciences, Vol 13, Iss 8, p 5115 (2023)
Cloud computing has received increasing attention due to its promise of delivering on-demand, scalable, and virtually unlimited resources. However, heterogeneity or co-location of virtual cloud resources can cause severe degradation of the efficiency
Externí odkaz:
https://doaj.org/article/1a1000650e26400c81dbe7a596f843e6
Akademický článek
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Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41:2156-2169
Sparse convolution neural networks (CNNs) are promising in reducing both memory usage and computational complexity while still preserving high inference accuracy. State-of-the-art sparse CNN accelerators can deliver high throughput by skipping zero w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5eabc24554c892af484bd10a7ad9c2c9
https://doi.org/10.1109/tcad.2021.3102191
https://doi.org/10.1109/tcad.2021.3102191
Publikováno v:
Information Sciences. 586:326-343
Stencil computation patterns are the backbone of many scientific and engineering simulations. The stencil computation is known to be constrained by its high demand of memory bandwidth, which limits performance on accelerators such as GPUs. Prior GPU-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b85ef6d1192b10ad8dd876b5b5f1eb8
https://doi.org/10.1016/j.ins.2021.11.042
https://doi.org/10.1016/j.ins.2021.11.042
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:251-262
The Deep Neural Network (DNN), Recurrent Neural Network (RNN) applications, rapidly becoming attractive to the market, process a large amount of low-locality data; thus, the memory bandwidth limits their peak performance. Therefore, many data centers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e8c17a2c50f4ec1bb1d53a4ec015b22
https://doi.org/10.1109/tpds.2021.3065365
https://doi.org/10.1109/tpds.2021.3065365
Autor:
Ye Yu, Niraj K. Jha
Publikováno v:
IEEE Transactions on Emerging Topics in Computing. 10:237-249
CNNs outperform traditional machine learning algorithms across a wide range of applications. However, their computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring dataflow style
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9ea6f6ca243bfc9cf5e09ecdbb1f8dd
https://doi.org/10.1109/tetc.2020.3003328
https://doi.org/10.1109/tetc.2020.3003328