000 | 04140nam a22005535i 4500 | ||
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001 | 978-3-031-01756-8 | ||
003 | DE-He213 | ||
005 | 20240730164224.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2017 sz | s |||| 0|eng d | ||
020 |
_a9783031017568 _9978-3-031-01756-8 |
||
024 | 7 |
_a10.1007/978-3-031-01756-8 _2doi |
|
050 | 4 | _aTK7867-7867.5 | |
072 | 7 |
_aTJFC _2bicssc |
|
072 | 7 |
_aTEC008010 _2bisacsh |
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072 | 7 |
_aTJFC _2thema |
|
082 | 0 | 4 |
_a621.3815 _223 |
100 | 1 |
_aReagen, Brandon. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983072 |
|
245 | 1 | 0 |
_aDeep Learning for Computer Architects _h[electronic resource] / _cby Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
|
300 |
_aXIV, 109 p. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSynthesis Lectures on Computer Architecture, _x1935-3243 |
|
505 | 0 | _aPreface -- Introduction -- Foundations of Deep Learning -- Methods and Models -- Neural Network Accelerator Optimization: A Case Study -- A Literature Survey and Review -- Conclusion -- Bibliography -- Authors' Biographies. | |
520 | _aMachine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloadsthemselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context. | ||
650 | 0 |
_aElectronic circuits. _919581 |
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650 | 0 |
_aMicroprocessors. _983073 |
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650 | 0 |
_aComputer architecture. _93513 |
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650 | 1 | 4 |
_aElectronic Circuits and Systems. _983076 |
650 | 2 | 4 |
_aProcessor Architectures. _983077 |
700 | 1 |
_aAdolf, Robert. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983078 |
|
700 | 1 |
_aWhatmough, Paul. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983079 |
|
700 | 1 |
_aWei, Gu-Yeon. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983080 |
|
700 | 1 |
_aBrooks, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _983081 |
|
710 | 2 |
_aSpringerLink (Online service) _983085 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031000546 |
776 | 0 | 8 |
_iPrinted edition: _z9783031006289 |
776 | 0 | 8 |
_iPrinted edition: _z9783031028847 |
830 | 0 |
_aSynthesis Lectures on Computer Architecture, _x1935-3243 _983086 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01756-8 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c85449 _d85449 |