國立虎尾科技大學 |

Design and Implementation of a Domain Specific Language for Deep Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Design and Implementation of a Domain Specific Language for Deep Learning./
作者:	Huang, Xiao Bing.
面頁冊數:	1 online resource (135 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355988000

Design and Implementation of a Domain Specific Language for Deep Learning.
Huang, Xiao Bing.

Design and Implementation of a Domain Specific Language for Deep Learning. - 1 online resource (135 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Milwaukee, 2018.

Includes bibliographical references

Deep Learning (DL) has found great success in well-diversified areas such as machine vision, speech recognition, big data analysis, and multimedia understanding recently. However, the existing state-of-the-art DL frameworks, e.g. Caffe2, Theano, TensorFlow, MxNet, Torch7, and CNTK, are programming libraries with fixed user interfaces, internal representations, and execution environments. Modifying the code of DL layers or data structure is very challenging without in-depth understanding of the underlying implementation. The optimization of the code and execution in these tools is often limited and relies on the specific DL computation graph manipulation and scheduling that lack systematic and universal strategies. Furthermore, most of these tools demand many dependencies beside the tool itself and require to be built to some specific platforms for DL training or inference.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355988000Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Design and Implementation of a Domain Specific Language for Deep Learning.
LDR:03334ntm a2200361K 4500 001 914819
005 20180724121430.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355988000
035 $a (MiAaPQ)AAI10792864
035 $a (MiAaPQ)uwm:12019
035 $a AAI10792864
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Huang, Xiao Bing. $3 1188184
245 1 0 $a Design and Implementation of a Domain Specific Language for Deep Learning.
264 0 $c 2018
300 $a 1 online resource (135 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Tian Zhao.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Milwaukee, 2018.
504 $a Includes bibliographical references
520 $a Deep Learning (DL) has found great success in well-diversified areas such as machine vision, speech recognition, big data analysis, and multimedia understanding recently. However, the existing state-of-the-art DL frameworks, e.g. Caffe2, Theano, TensorFlow, MxNet, Torch7, and CNTK, are programming libraries with fixed user interfaces, internal representations, and execution environments. Modifying the code of DL layers or data structure is very challenging without in-depth understanding of the underlying implementation. The optimization of the code and execution in these tools is often limited and relies on the specific DL computation graph manipulation and scheduling that lack systematic and universal strategies. Furthermore, most of these tools demand many dependencies beside the tool itself and require to be built to some specific platforms for DL training or inference.
520 $a This dissertation presents DeepDSL, a domain specific language (DSL) embedded in Scala, that compiles DL networks encoded with DeepDSL to efficient, compact, and portable Java source programs for DL training and inference. DeepDSL represents DL networks as abstract tensor functions, performs symbolic gradient derivations to generate the Intermediate Representation (IR), optimizes the IR expressions, and compiles the optimized IR expressions to cross-platform Java code that is easily modifiable and debuggable. Also, the code directly runs on GPU without additional dependencies except a small set of JNI (Java Native Interface) wrappers for invoking the underneath GPU libraries. Moreover, DeepDSL provides static analysis for memory consumption and error detection.
520 $a DeepDSL has been evaluated with many current state-of-the-art DL networks (e.g. Alexnet, GoogleNet, VGG, Overfeat, and Deep Residual Network). While the DSL code is highly compact with less than 100 lines for each of the network, the Java source code generated by the DeepDSL compiler is highly efficient. Our experiments show that the output java source has very competitive runtime performance and memory efficiency compared to the existing DL frameworks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Wisconsin - Milwaukee. $b Computer Science. $3 1180251
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792864 $z click for full text (PQDT)