標(biāo)題: Titlebook: Efficient Processing of Deep Neural Networks; Vivienne Sze,Yu-Hsin Chen,Joel S. Emer Book 2020 Springer Nature Switzerland AG 2020 [打印本頁] 作者: peak-flow-meter 時間: 2025-3-21 19:15
書目名稱Efficient Processing of Deep Neural Networks影響因子(影響力)
書目名稱Efficient Processing of Deep Neural Networks影響因子(影響力)學(xué)科排名
書目名稱Efficient Processing of Deep Neural Networks網(wǎng)絡(luò)公開度
書目名稱Efficient Processing of Deep Neural Networks網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Efficient Processing of Deep Neural Networks被引頻次
書目名稱Efficient Processing of Deep Neural Networks被引頻次學(xué)科排名
書目名稱Efficient Processing of Deep Neural Networks年度引用
書目名稱Efficient Processing of Deep Neural Networks年度引用學(xué)科排名
書目名稱Efficient Processing of Deep Neural Networks讀者反饋
書目名稱Efficient Processing of Deep Neural Networks讀者反饋學(xué)科排名
作者: 憤世嫉俗者 時間: 2025-3-21 21:55
https://doi.org/10.1007/978-3-662-39613-1tion of DNNs to speech recognition [6] and image recognition. [7], the number of applications that use DNNs has exploded. These DNNs are employed in a myriad of applications from self-driving cars [8], to detecting cancer [9], to playing complex games [10]. In many of these domains, DNNs are now abl作者: 省略 時間: 2025-3-22 00:49
Die Sicherung der Baugrubenwandungenapidly to improve accuracy and efficiency. In all cases, the input to a DNN is a set of values representing the information to be analyzed by the network. For instance, these values can be pixels of an image, sampled amplitudes of an audio wave, or the numerical representation of the state of some s作者: RENIN 時間: 2025-3-22 06:57
Operationen am Darm. Appendektomiekey metrics that one should consider when comparing and evaluating the strengths and weaknesses of different designs and proposed techniques and that should be incorporated into design considerations. While efficiency is often only associated with the number of operations per second per Watt (e.g., 作者: PRO 時間: 2025-3-22 08:58
Operationen am Darm. Appendektomiele dependencies between these operations and the accumulations are commutative, there is considerable flexibility in the order in which MACs can be scheduled and these computations can be easily parallelized. Therefore, in order to achieve high performance for DNNs, highly parallel compute paradigms作者: enormous 時間: 2025-3-22 13:01
https://doi.org/10.1007/978-3-642-49774-2multiplications, in order to achieve higher performance (i.e., higher throughput and/or lower latency) on off-the-shelf general-purpose processors such as CPUs and GPUs. In this chapter, we will focus on optimizing the processing of DNNs directly by designing specialized hardware.作者: enormous 時間: 2025-3-22 20:35 作者: acolyte 時間: 2025-3-22 22:24 作者: resistant 時間: 2025-3-23 03:04
https://doi.org/10.1007/978-3-642-91000-5eferring to the fact that there are many repeated values in the data. Much of the time the repeated value is zero, which is what we will assume unless explicitly noted. Thus, we will talk about the sparsity or density of the data as the percentage of zeros or non-zeros, respectively in the data. The作者: 痛打 時間: 2025-3-23 07:49 作者: capillaries 時間: 2025-3-23 10:51 作者: Hay-Fever 時間: 2025-3-23 15:58
Therapie der Harnwegsinfekte bei Kindern,ations including computer vision, speech recognition, and robotics and are often delivering better than human accuracy. However, while DNNs can deliver this outstanding accuracy, it comes at the cost of high computational complexity. With the stagnation of improvements in general-purpose computation作者: 不確定 時間: 2025-3-23 22:00
Overview of Deep Neural Networksapidly to improve accuracy and efficiency. In all cases, the input to a DNN is a set of values representing the information to be analyzed by the network. For instance, these values can be pixels of an image, sampled amplitudes of an audio wave, or the numerical representation of the state of some system or game.作者: 和平主義者 時間: 2025-3-23 23:37
Designing DNN Acceleratorsmultiplications, in order to achieve higher performance (i.e., higher throughput and/or lower latency) on off-the-shelf general-purpose processors such as CPUs and GPUs. In this chapter, we will focus on optimizing the processing of DNNs directly by designing specialized hardware.作者: dapper 時間: 2025-3-24 03:30
Operation Mapping on Specialized Hardwaree notion of the . of the computation for a particular workload layer shape onto a specific DNN accelerator design, and the fact that the compiler-like process of picking the right mapping is important to optimize behavior with respect to energy efficiency and/or performance.作者: PHIL 時間: 2025-3-24 08:58 作者: 奴才 時間: 2025-3-24 14:04 作者: Servile 時間: 2025-3-24 15:51
Efficient Processing of Deep Neural Networks978-3-031-01766-7Series ISSN 1935-3235 Series E-ISSN 1935-3243 作者: Feature 時間: 2025-3-24 22:12
Die Sicherung der Baugrubenwandungenapidly to improve accuracy and efficiency. In all cases, the input to a DNN is a set of values representing the information to be analyzed by the network. For instance, these values can be pixels of an image, sampled amplitudes of an audio wave, or the numerical representation of the state of some system or game.作者: 沒有貧窮 時間: 2025-3-25 03:12
https://doi.org/10.1007/978-3-642-49774-2multiplications, in order to achieve higher performance (i.e., higher throughput and/or lower latency) on off-the-shelf general-purpose processors such as CPUs and GPUs. In this chapter, we will focus on optimizing the processing of DNNs directly by designing specialized hardware.作者: 賞錢 時間: 2025-3-25 05:11
Hartwig Steffenhagen,Michael Stillere notion of the . of the computation for a particular workload layer shape onto a specific DNN accelerator design, and the fact that the compiler-like process of picking the right mapping is important to optimize behavior with respect to energy efficiency and/or performance.作者: 猛然一拉 時間: 2025-3-25 08:17
D. Bach,R. Hartung,W. Vahlensiecks well as the transfer of the data. The associated physical factors also limit the bandwidth available to deliver data between memory and compute, and thus limits the throughput of the overall system. This is commonly referred to by computer architects as the “memory wall.”作者: 無價值 時間: 2025-3-25 14:08 作者: cathartic 時間: 2025-3-25 15:50 作者: 金盤是高原 時間: 2025-3-25 22:49
1935-3235 lying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary wor978-3-031-00638-8978-3-031-01766-7Series ISSN 1935-3235 Series E-ISSN 1935-3243 作者: HUMID 時間: 2025-3-26 02:42 作者: albuminuria 時間: 2025-3-26 06:57 作者: escalate 時間: 2025-3-26 11:11
Overview of Deep Neural Networksapidly to improve accuracy and efficiency. In all cases, the input to a DNN is a set of values representing the information to be analyzed by the network. For instance, these values can be pixels of an image, sampled amplitudes of an audio wave, or the numerical representation of the state of some s作者: 雄辯 時間: 2025-3-26 13:12
Key Metrics and Design Objectiveskey metrics that one should consider when comparing and evaluating the strengths and weaknesses of different designs and proposed techniques and that should be incorporated into design considerations. While efficiency is often only associated with the number of operations per second per Watt (e.g., 作者: Ankylo- 時間: 2025-3-26 17:12 作者: BRINK 時間: 2025-3-26 22:41
Designing DNN Acceleratorsmultiplications, in order to achieve higher performance (i.e., higher throughput and/or lower latency) on off-the-shelf general-purpose processors such as CPUs and GPUs. In this chapter, we will focus on optimizing the processing of DNNs directly by designing specialized hardware.作者: Liberate 時間: 2025-3-27 04:28 作者: Customary 時間: 2025-3-27 07:23 作者: intuition 時間: 2025-3-27 10:06
Exploiting Sparsityeferring to the fact that there are many repeated values in the data. Much of the time the repeated value is zero, which is what we will assume unless explicitly noted. Thus, we will talk about the sparsity or density of the data as the percentage of zeros or non-zeros, respectively in the data. The作者: delegate 時間: 2025-3-27 15:11 作者: Pepsin 時間: 2025-3-27 18:19
Advanced Technologiess well as the transfer of the data. The associated physical factors also limit the bandwidth available to deliver data between memory and compute, and thus limits the throughput of the overall system. This is commonly referred to by computer architects as the “memory wall.”作者: Ingredient 時間: 2025-3-28 00:58
Conclusionations including computer vision, speech recognition, and robotics and are often delivering better than human accuracy. However, while DNNs can deliver this outstanding accuracy, it comes at the cost of high computational complexity. With the stagnation of improvements in general-purpose computation作者: enflame 時間: 2025-3-28 03:04 作者: Bumptious 時間: 2025-3-28 06:53 作者: 富饒 時間: 2025-3-28 13:11
https://doi.org/10.1007/978-3-662-39613-1stical learning on a large amount of data to obtain an effective representation of an input space. This is different from earlier approaches that use hand-crafted features or rules designed by experts.作者: HPA533 時間: 2025-3-28 14:36 作者: 問到了燒瓶 時間: 2025-3-28 21:32
Introductionstical learning on a large amount of data to obtain an effective representation of an input space. This is different from earlier approaches that use hand-crafted features or rules designed by experts.作者: Conserve 時間: 2025-3-29 01:06
Key Metrics and Design Objectivescs including accuracy, throughput, latency, energy consumption, power consumption, cost, flexibility, and scalability. Reporting a comprehensive set of these metrics is important in order to provide a complete picture of the trade-offs made by a proposed design or technique.作者: Limited 時間: 2025-3-29 03:21 作者: Defraud 時間: 2025-3-29 09:56
Designing Efficient DNN Models other co-design approaches, the main challenge is to improve the efficiency of the network architecture as evaluated by the metrics described in Chapter 3, such as energy consumption and latency, without sacrificing the accuracy.作者: 哀求 時間: 2025-3-29 12:51
