標(biāo)題: Titlebook: Data Orchestration in Deep Learning Accelerators; Tushar Krishna,Hyoukjun Kwon,Ananda Samajdar Book 2020 Springer Nature Switzerland AG 20 [打印本頁(yè)] 作者: 從未迷惑 時(shí)間: 2025-3-21 17:19
書目名稱Data Orchestration in Deep Learning Accelerators影響因子(影響力)
書目名稱Data Orchestration in Deep Learning Accelerators影響因子(影響力)學(xué)科排名
書目名稱Data Orchestration in Deep Learning Accelerators網(wǎng)絡(luò)公開度
書目名稱Data Orchestration in Deep Learning Accelerators網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Orchestration in Deep Learning Accelerators被引頻次
書目名稱Data Orchestration in Deep Learning Accelerators被引頻次學(xué)科排名
書目名稱Data Orchestration in Deep Learning Accelerators年度引用
書目名稱Data Orchestration in Deep Learning Accelerators年度引用學(xué)科排名
書目名稱Data Orchestration in Deep Learning Accelerators讀者反饋
書目名稱Data Orchestration in Deep Learning Accelerators讀者反饋學(xué)科排名
作者: Malfunction 時(shí)間: 2025-3-21 21:55
Dataflow and Data Reuse,to billions of computations, we cannot fit all of the computations within an accelerator, which typically has hundreds to thousands of compute units. Therefore, we need to slice the problem into smaller chunks (i.e., computation tiles) and run them in a certain order (i.e., tile scheduling). Within 作者: antedate 時(shí)間: 2025-3-22 01:21
Buffer Hierarchies,ic accelerators have constraints and goals that differ in key ways. It is important to understand in detail how these cause accelerator architects to make different hardware choices. In this chapter, we present a framework for understanding key options, and explore tradeoffs between design effort an作者: entreat 時(shí)間: 2025-3-22 07:24
Networks-on-Chip, contain an array of hundreds of PEs. These accelerators aim to achieve high throughput by exploiting massive parallel computations over the PEs while keeping the cost-of-operation much lower than off-the-shelf components with the same compute budget. However, adding more compute elements in an acce作者: 職業(yè) 時(shí)間: 2025-3-22 11:50 作者: Restenosis 時(shí)間: 2025-3-22 13:00 作者: Restenosis 時(shí)間: 2025-3-22 19:12 作者: 被告 時(shí)間: 2025-3-23 00:52
Buffer Hierarchies,ic accelerators have constraints and goals that differ in key ways. It is important to understand in detail how these cause accelerator architects to make different hardware choices. In this chapter, we present a framework for understanding key options, and explore tradeoffs between design effort and cross-project reuse.作者: 尾巴 時(shí)間: 2025-3-23 02:15
Jason Gu,Rajeeb Dey,Nabanita Adhikaryrovide a brief background on Deep Neural Networks (DNNs), which are the underlying computational mechanisms within Deep Learning applications. Our objective is not to go into the theory behind the structure and accuracy of DNNs (which readers can find in any modern textbook on Machine Learning or De作者: SPASM 時(shí)間: 2025-3-23 06:55
and the Co-production of Men’s Healthto billions of computations, we cannot fit all of the computations within an accelerator, which typically has hundreds to thousands of compute units. Therefore, we need to slice the problem into smaller chunks (i.e., computation tiles) and run them in a certain order (i.e., tile scheduling). Within 作者: Expediency 時(shí)間: 2025-3-23 10:35 作者: 手段 時(shí)間: 2025-3-23 16:49
Charlene Elliott,Josh Greenberg contain an array of hundreds of PEs. These accelerators aim to achieve high throughput by exploiting massive parallel computations over the PEs while keeping the cost-of-operation much lower than off-the-shelf components with the same compute budget. However, adding more compute elements in an acce作者: delegate 時(shí)間: 2025-3-23 21:28 作者: AMOR 時(shí)間: 2025-3-24 01:44 作者: Factual 時(shí)間: 2025-3-24 04:28 作者: Atheroma 時(shí)間: 2025-3-24 09:28
978-3-031-00639-5Springer Nature Switzerland AG 2020作者: 免費(fèi) 時(shí)間: 2025-3-24 10:41
Challenges in Vaccine CommunicationIn the previous chapters we have discussed the various crucial parts of building a neural network accelerator one by one. In this chapter we zoom out and conceptualize how all the pieces bind together and work as a synergistic system optimized for various design goals.作者: unstable-angina 時(shí)間: 2025-3-24 15:03 作者: 小淡水魚 時(shí)間: 2025-3-24 19:39 作者: 過(guò)分 時(shí)間: 2025-3-24 23:22
Data Orchestration in Deep Learning Accelerators978-3-031-01767-4Series ISSN 1935-3235 Series E-ISSN 1935-3243 作者: diathermy 時(shí)間: 2025-3-25 06:09
and the Co-production of Men’s Healthic accelerators have constraints and goals that differ in key ways. It is important to understand in detail how these cause accelerator architects to make different hardware choices. In this chapter, we present a framework for understanding key options, and explore tradeoffs between design effort and cross-project reuse.作者: 發(fā)誓放棄 時(shí)間: 2025-3-25 09:26 作者: OREX 時(shí)間: 2025-3-25 12:56 作者: obstinate 時(shí)間: 2025-3-25 17:12 作者: 摘要記錄 時(shí)間: 2025-3-25 22:59
Dataflow and Data Reuse,ning). We show how choices in tiling, scheduling, and partitioning affect the degree to which an accelerator can exploit the . reuse present in the original problem, and formalize these choices into the concepts of . and ..作者: Charitable 時(shí)間: 2025-3-26 01:11 作者: 魅力 時(shí)間: 2025-3-26 07:42 作者: 草本植物 時(shí)間: 2025-3-26 09:32 作者: NATAL 時(shí)間: 2025-3-26 15:05
Communication in Adopting Moral Normsial step in the design-space exploration loop for DNN accelerators. In this chapter, we discuss the mapping step in further detail, and then examine how microarchitectural models for DNN accelerators can be constructed to evaluate the performance and energy cost of execution of a mapping on the hardware.作者: 不在灌木叢中 時(shí)間: 2025-3-26 17:31 作者: 新鮮 時(shí)間: 2025-3-26 21:04
Modeling Accelerator Design Space,ial step in the design-space exploration loop for DNN accelerators. In this chapter, we discuss the mapping step in further detail, and then examine how microarchitectural models for DNN accelerators can be constructed to evaluate the performance and energy cost of execution of a mapping on the hardware.作者: 中國(guó)紀(jì)念碑 時(shí)間: 2025-3-27 03:08 作者: 柳樹；枯黃 時(shí)間: 2025-3-27 08:40 作者: FAST 時(shí)間: 2025-3-27 12:33 作者: 公豬 時(shí)間: 2025-3-27 14:07
https://doi.org/10.1007/978-981-33-6664-0esignflow involved in DNN accelerator design via case studies, and also highlighted the role of microarchitectural models and mappers during the design and deployment process. Finally, we gave a glimpse of the challenges and opportunities in designing accelerators for sparse and compressed DNNs.作者: exhilaration 時(shí)間: 2025-3-27 20:13 作者: Anticoagulants 時(shí)間: 2025-3-27 22:34 作者: Amorous 時(shí)間: 2025-3-28 06:02
0302-9743 Overview: 978-3-540-10286-1978-3-540-38421-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Indicative 時(shí)間: 2025-3-28 09:26
Local Theory of Several Complex Variables,etry. Further, we like to show how they fit into the general context of singularities. This requires some knowledge of complex analysis of several variables. This chapter does not present a full discussion of the topic. It is rather a concise introduction which covers the general background as well 作者: glowing 時(shí)間: 2025-3-28 13:52
Metamedia, Ecosystems and Value Chainsatsache, die das Fehlen einer zusammenfassenden Darstellung der Entwicklung der technischen Hochschulen ?sterreichs noch bedauerlicher empfinden l??t, hat uns bewogen, gerade die Entwicklung der h?heren technischen Lehranstalten umfassender vorzuführen, als dies sonst erforderlich gewesen w?re.
