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Kévin Mambu, Henri-Pierre Charles, Maha Kooli and Julie Dumas
In-memory computing (IMC) aims to solve the performance gap between CPU and memories introduced by the memory wall. However, it does not address the energy wall problem caused by data transfer over memory hierarchies. This paper proposes the data-localit...
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Arghavan Asad, Rupinder Kaur and Farah Mohammadi
From self-driving cars to detecting cancer, the applications of modern artificial intelligence (AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able to extract high-level features after the network has been trained usi...
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Wei Chek Moon, Lariyah Mohd Sidek, Tze Liang Lau, How Tion Puay, Taksiah Abdul Majid, Ahmad Khairi Abd Wahab and Fang Yenn Teo
The tsunami is one of the deadliest natural disasters, responsible for more than 260,000 deaths and billions in economic losses over the last two decades. The footage of the devastating power of the 2004 Indian Ocean tsunami perhaps remains vivid in the ...
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Earle Jennings
Pág. 54 - 70
This paper introduces a fundamentally new computer architecture for supercomputers. The core module is application compatible with an existing superscalar microprocessor, with minimized energy use, and is optimized for local sparse matrix operation...
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Roman Kaplan,Leonid Yavits,Ran Ginosar
Pág. 99 - 116
Near-data in-memory processing research has been gaining momentum in recent years. Typical processing-in-memory architecture places a single or several processing elements next to a volatile memory, enabling processing without transferring data to the ho...
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Jacob, P.; Zia, A.; Erdogan, O.; Belemjian, P.M.; Jin-Woo Kim; Chu, M.; Kraft, R.P.; McDonald, J.F.; Bernstein, K.
Pág. 108 - 122
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