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Titlebook: Advanced Computer Architecture; 13th Conference, ACA Dezun Dong,Xiaoli Gong,Junjie Wu Conference proceedings 2020 Springer Nature Singapore

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發(fā)表于 2025-3-25 04:07:58 | 只看該作者
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發(fā)表于 2025-3-25 07:59:53 | 只看該作者
Peter Ritter von Tunner und seine Schule,irms to address the security problems of SCADA. Especially, after the software defined network (SDN) arose, it has become a beneficial attempt to improve the SCADA security. In this paper, a formalized vulnerability detection platform named SDNVD-SCADA is presented based on the SDN technology, which
23#
發(fā)表于 2025-3-25 12:35:00 | 只看該作者
Zur Geschichte der Dynamomaschine,interconnect network of high performance computing systems. However, with the rapid development of network switching chips towards the higher radix, the traditional in-band management implementation of ring structure faces the problem of delay performance scalability. The work proposed two optimized
24#
發(fā)表于 2025-3-25 19:14:42 | 只看該作者
Zur Geschichte der Dynamomaschine,l integrity, both a Continuous Time Linear Equalizer (CTLE) and Feed Forward Equalizer (FFE) are adapted. To save power dissipation, a quarter-rate based 3-tap FFE is proposed. To reduce the chip area, a Band-Band Phase Discriminator (BBPD) based PI CDR is employed. In addition, a 2-order digital fi
25#
發(fā)表于 2025-3-25 23:59:46 | 只看該作者
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發(fā)表于 2025-3-26 02:43:33 | 只看該作者
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發(fā)表于 2025-3-26 05:33:51 | 只看該作者
Vanuccio Biringuccio (um 1540 n. Chr.),rence, it is challenging to employ GNN to process large-scale graphs. Fortunately, processing-in-memory (PIM) architecture has been widely investigated as a promising approach to address the “Memory Wall”. In this work, we propose a PIM architecture to accelerate GNN inference. We develop an optimiz
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發(fā)表于 2025-3-26 10:57:43 | 只看該作者
29#
發(fā)表于 2025-3-26 14:18:08 | 只看該作者
30#
發(fā)表于 2025-3-26 18:35:37 | 只看該作者
,Leonardo da Vinci (1452–1519),BN (Batch Normalization) layer’s execution time is increasing and even exceeds the convolutional layer. The BN layer can accelerate the convergence of training. However, little work focus on the efficient hardware implementation of BN layer computation in training. In this work, we propose an accele
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