漢德百科全書 | 汉德百科全书

       
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Catalog IT-Times

Peripheral Component Interconnect Special Interest Group,PCI-SIG

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Hauptprogramm
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Host
Host Prozessor

Von 'System/360' (S/360) zu ESA/390 und z/-Architektur

1964 S/360

– CISC, 24bit Adressierung, 'Real Storage', Uniprozessoren– Amdahl, G.M., Blaauw, G.A., and Brooks, F.P.: Architecture of the IBM System/360

1971 S/370

– 'Virtual Storage', Multiprozessor-Unterstützung, ...1981 S/370 XA (Extended Architecture)– 31bit Adressierung (2GB), 'Expanded Storage' (>2GB), 'Channel Subsystem'– 'Interpretive Execution': Basis für Logische Partitionierung ('LPAR')

1988 ESA/370

– ESA = Enterprise Systems Architecture, Logische Partitionierung– Ausbau der Speicher-Zugriffsmethoden: Mehr als ein 'address space‘

1990 ESA/390

– 'ESCON' (Enterprise Systems Connection Architecture) Glasfasertechnologie ...– Datenkompression, Kryptographie, LPAR Erweiterungen

1994 Parallel Sysplex, Übergang von Bipolar zu CMOS Technologie

– 'Coupling Facility', Cluster von bis zu 32 x 16-way MultiProzessoren– 'FICON' (Fiber Channel Connectivity), Ausbau der Glasfasertechnologie

2000 z/-Architektur (64-bit),z900, z800,z990(2003),z890(2004),z9 EC(2005),z9 BC(2006)

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Hauptplatine
engl. mainboard, auch motherboard
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Sumitomo Electric Industries
1897
 
 
 
 
 
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Sumitomo´s Proton Therapy System
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Sumitomo Heavy Industries
1888
 
 
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Balkendiagramm
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Key Generator
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State Key Laboratory of ASIC and System
 
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Transformer
Ein Transformer ist eine von Google entwickelte Deep-Learning-Architektur, die einen sog. Aufmerksamkeitsmechanismus integriert, der erstmals in einem Artikel aus dem Jahr 2017 „Attention Is All You Need“ vorgestellt wurde. Dabei wird Text wird in numerische Darstellungen umgewandelt, die als Token bezeichnet werden, anschließend durch Worteinbettung in Vektoren umgewandelt.

Transformer模型(直译为“变换器”)是一种采用注意力机制深度学习模型,这一机制可以按输入数据各部分重要性的不同而分配不同的权重。该模型主要用于自然语言处理(NLP)与计算机视觉(CV)领域。[1]

循环神经网络(RNN)一样,Transformer模型旨在处理自然语言等顺序输入数据,可应用于翻译、文本摘要等任务。而与RNN不同的是,Transformer模型能够一次性处理所有输入数据。注意力机制可以为输入序列中的任意位置提供上下文。如果输入数据是自然语言,则Transformer不必像RNN一样一次只处理一个单词,这种架构允许更多的并行计算,并以此减少训练时间。

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Konvertierungsprogramm
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