| 書目名稱 | Deep Learning Systems | | 副標(biāo)題 | Algorithms, Compiler | | 編輯 | Andres Rodriguez | | 視頻video | http://file.papertrans.cn/265/264580/264580.mp4 | | 叢書名稱 | Synthesis Lectures on Computer Architecture | | 圖書封面 |  | | 描述 | This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of pro | | 出版日期 | Book 2021 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01769-8 | | isbn_softcover | 978-3-031-00641-8 | | isbn_ebook | 978-3-031-01769-8Series ISSN 1935-3235 Series E-ISSN 1935-3243 | | issn_series | 1935-3235 | | copyright | Springer Nature Switzerland AG 2021 |
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