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: 4 يناير 2022 → 7 يناير 2022

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	4/01/22 → 7/01/22

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

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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.",

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

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year = "2022",

month = jan,

day = "4",

doi = "10.1145/3491003.3498354",

language = "אנגלית",

series = "ACM International Conference Proceeding Series",

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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, 4/01/22. 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

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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.

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KW - Graph Coloring

KW - Memory Management

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Memory Allocation for Neural Networks using Graph Coloring

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