US 11,726,754 B2
General machine learning model, and model file generation and parsing method
Weijian Du, Pudong New Area (CN); Linyang Wu, Pudong New Area (CN); and Xunyu Chen, Pudong New Area (CN)
Assigned to SHANGHAI CAMBRICON INFORMATION TECHNOLOGY CO., LTD., Pudong New Area (CN)
Filed by Shanghai Cambricon Information Technology Co., Ltd., Pudong New Area (CN)
Filed on Jun. 26, 2022, as Appl. No. 17/849,650.
Application 17/849,650 is a continuation of application No. 17/130,393, filed on Dec. 22, 2020, granted, now 11,403,080.
Application 17/130,393 is a continuation of application No. 16/975,082, granted, now 11,334,329, issued on May 17, 2022, previously published as PCT/CN2019/085853, filed on May 7, 2019.
Claims priority of application No. 201810588623.3 (CN), filed on Jun. 8, 2018; application No. 201810589390.9 (CN), filed on Jun. 8, 2018; application No. 201811456246.4 (CN), filed on Nov. 30, 2018; application No. 201811457719.2 (CN), filed on Nov. 30, 2018; application No. 201811459679.5 (CN), filed on Nov. 30, 2018; and application No. 201811459853.6 (CN), filed on Nov. 30, 2018.
Prior Publication US 2022/0326919 A1, Oct. 13, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 8/35 (2018.01); G06F 8/41 (2018.01); G06F 8/10 (2018.01)
CPC G06F 8/433 (2013.01) [G06F 8/10 (2013.01); G06F 8/35 (2013.01); G06F 8/447 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for processing a machine learning task, the method comprising:
acquiring task parameters of a machine learning task;
processing task parameters to obtain shareable data and unshareable data, wherein the shareable data refers to data shared among cores in a multi-core platform, and the unshareable data refers to data that is not shared among cores in the multi-core platform;
arranging the shareable data to obtain a heap data block, and arranging the unshareable data to obtain a stack data block; and
packing the heap data block and the stack data block to obtain a general-purpose machine learning model;
wherein the shareable data comprises shareable model parameters processed by the task parameters and task instructions; the shareable model parameters include model parameter static data that does not change during running of the machine learning task and model parameter dynamic data that changes during the running of the machine learning task;
arranging the shareable data to obtain a heap data block comprising:
packing and integrating task instructions and model parameter static data to obtain a successive static data block;
packaging and integrating the model parameter dynamic data to obtain a successive dynamic data block; and
packing and integrating the successive static data block, the successive dynamic data block to obtain the heap data block;
obtaining data attributes of input data, data attributes of output data, and data attribute of intermediate result temporary space of the model parameters;
storing in a storage space of the input data and in a storage space of the output data as the sharable data, and storing the intermediate result temporary space as unshareable data.