抛物线 发表于 2025-3-25 04:07:58
http://reply.papertrans.cn/15/1454/145350/145350_21.pngBarrister 发表于 2025-3-25 07:59:53
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, whichInstinctive 发表于 2025-3-25 12:35:00
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 optimizedhypnotic 发表于 2025-3-25 19:14:42
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 fiAnonymous 发表于 2025-3-25 23:59:46
http://reply.papertrans.cn/15/1454/145350/145350_25.png辩论的终结 发表于 2025-3-26 02:43:33
http://reply.papertrans.cn/15/1454/145350/145350_26.pngAXIOM 发表于 2025-3-26 05:33:51
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 optimizDawdle 发表于 2025-3-26 10:57:43
http://reply.papertrans.cn/15/1454/145350/145350_28.pngBadger 发表于 2025-3-26 14:18:08
http://reply.papertrans.cn/15/1454/145350/145350_29.pngBOOR 发表于 2025-3-26 18:35:37
,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