MEMORY REDUCTION FOR NEURAL NETWORKS WITH FIXED STRUCTURES

Patent №

US 10,782,897

Granted

2020-09-22

Filed 2018

Owner

INTERNATIONAL BUSINESS MACHINES CORPORATION

AI components

3

ml · planning · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

15943079

A method is provided for reducing consumption of a memory in a propagation process for a neural network (NN) having fixed structures for computation order and node data dependency. The memory includes memory segments for allocating to nodes. The method collects, in a NN training iteration, information for each node relating to an allocation, size, and lifetime thereof. The method chooses, responsive to the information, a first node having a maximum memory size relative to remaining nodes, and a second node non-overlapped with the first node lifetime. The method chooses another node non-overlapped with the first node lifetime, responsive to a sum of memory sizes of the second node and the other node not exceeding a first node memory size. The method reallocates a memory segment allocated to the first node to the second node and the other node to be reused by the second node and the other node.

Machine learningPlanningAI hardwareG06F 3/0626G06N 3/084G06F 3/0631G06F 3/0673G06N 3/045G06N 3/0464G06N 3/063G06N 3/08+1 more

AI classification

AI hardware1.00
Machine learning1.00
Planning0.88
Evolutionary computation0.25
Knowledge representation0.08
Natural language0.01
Vision0.00
Speech0.00

Ownership

INTERNATIONAL BUSINESS MACHINES CORPORATION

assignment · 454110202

Assignors

SEKIYAMA, TARO, IMAI, HARUKI, DOI, JUN, NEGISHI, YASUSHI

On an employer assignment, the assignors are typically the inventors.

From the same owner

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