Memory Allocation for Neural Networks using Graph Coloring

Leonid Barenboim; Rami Drucker; Oleg Zatulovsky; Eli Levi

doi:10.1145/3491003.3498354

Memory Allocation for Neural Networks using Graph Coloring

Leonid Barenboim, Rami Drucker, Oleg Zatulovsky, Eli Levi

מדעי המחשב

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

ملخص

The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.

اللغة الأصلية	الإنجليزيّة
عنوان منشور المضيف	ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking
ناشر	Association for Computing Machinery
الصفحات	232-233
عدد الصفحات	2
رقم المعيار الدولي للكتب (الإلكتروني)	9781450395601
المعرِّفات الرقمية للأشياء	https://doi.org/10.1145/3491003.3498354
حالة النشر	نُشِر - 4 يناير 2022
الحدث	23rd International Conference on Distributed Computing and Networking, ICDCN 2022 - Virtual, Online, الهند المدة: ٤ يناير ٢٠٢٢ → ٧ يناير ٢٠٢٢

سلسلة المنشورات

الاسم	ACM International Conference Proceeding Series

!!Conference

!!Conference	23rd International Conference on Distributed Computing and Networking, ICDCN 2022
الدولة/الإقليم	الهند
المدينة	Virtual, Online
المدة	٤/٠١/٢٢ → ٧/٠١/٢٢

ملاحظة ببليوغرافية

Publisher Copyright:
© 2022 Owner/Author.

الوصول إلى المستند

10.1145/3491003.3498354

الملفات والروابط الأخرى

Link to publication in Scopus

قم بذكر هذا

Barenboim, L., Drucker, R., Zatulovsky, O., & Levi, E. (2022). Memory Allocation for Neural Networks using Graph Coloring. في ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking (الصفحات 232-233). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3491003.3498354

@inproceedings{fb2c0c1f473a43fd8880d145ad9ec603,

title = "Memory Allocation for Neural Networks using Graph Coloring",

abstract = "The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.",

keywords = "Graph Coloring, Memory Management, Neural Networks",

author = "Leonid Barenboim and Rami Drucker and Oleg Zatulovsky and Eli Levi",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 23rd International Conference on Distributed Computing and Networking, ICDCN 2022 ; Conference date: 04-01-2022 Through 07-01-2022",

year = "2022",

month = jan,

day = "4",

doi = "10.1145/3491003.3498354",

language = "אנגלית",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "232--233",

booktitle = "ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking",

}

Barenboim, L, Drucker, R, Zatulovsky, O & Levi, E 2022, Memory Allocation for Neural Networks using Graph Coloring. في ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking. ACM International Conference Proceeding Series, Association for Computing Machinery, الصفحات 232-233, 23rd International Conference on Distributed Computing and Networking, ICDCN 2022, Virtual, Online, الهند, ٤/٠١/٢٢. https://doi.org/10.1145/3491003.3498354

Memory Allocation for Neural Networks using Graph Coloring. / Barenboim, Leonid; Drucker, Rami; Zatulovsky, Oleg وآخرون.
ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking. Association for Computing Machinery, 2022. صفحة 232-233 (ACM International Conference Proceeding Series).

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

TY - GEN

T1 - Memory Allocation for Neural Networks using Graph Coloring

AU - Barenboim, Leonid

AU - Drucker, Rami

AU - Zatulovsky, Oleg

AU - Levi, Eli

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2022/1/4

Y1 - 2022/1/4

N2 - The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.

AB - The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.

KW - Graph Coloring

KW - Memory Management

KW - Neural Networks

UR - http://www.scopus.com/inward/record.url?scp=85123997013&partnerID=8YFLogxK

U2 - 10.1145/3491003.3498354

DO - 10.1145/3491003.3498354

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85123997013

T3 - ACM International Conference Proceeding Series

SP - 232

EP - 233

BT - ICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking

PB - Association for Computing Machinery

T2 - 23rd International Conference on Distributed Computing and Networking, ICDCN 2022

Y2 - 4 January 2022 through 7 January 2022

ER -

Memory Allocation for Neural Networks using Graph Coloring

ملخص

سلسلة المنشورات

!!Conference

ملاحظة ببليوغرافية

الوصول إلى المستند

الملفات والروابط الأخرى

بصمة

قم بذكر هذا