Titlebook: Efficient Processing of Deep Neural Networks; Vivienne Sze,Yu-Hsin Chen,Joel S. Emer Book 2020 Springer Nature Switzerland AG 2020

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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

憤世嫉俗者

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

省略

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

RENIN

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.,

PRO

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

enormous

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.

enormous

acolyte

resistant

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

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