The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 28, 2023
Filed:
Apr. 28, 2017
Intel Corporation, Santa Clara, CA (US);
Abhishek R. Appu, El Dorado Hills, CA (US);
John C. Weast, Portland, OR (US);
Sara S. Baghsorkhi, San Jose, CA (US);
Justin E. Gottschlich, Santa Clara, CA (US);
Prasoonkumar Surti, Folsom, CA (US);
Chandrasekaran Sakthivel, Sunnyvale, CA (US);
Altug Koker, El Dorado Hills, CA (US);
Farshad Akhbari, Chandler, AZ (US);
Feng Chen, Shanghai, CN;
Dukhwan Kim, San Jose, CA (US);
Narayan Srinivasa, Portland, CA (US);
Nadathur Rajagopalan Satish, Santa Clara, CA (US);
Kamal Sinha, Rancho Cordova, CA (US);
Joydeep Ray, Folsom, CA (US);
Balaji Vembu, Folsom, CA (US);
Mike B. Macpherson, Portland, OR (US);
Linda L. Hurd, Cool, CA (US);
Sanjeev Jahagirdar, Folsom, CA (US);
Vasanth Ranganathan, El Dorado Hills, CA (US);
INTEL CORPORATION, Santa Clara, CA (US);
Abstract
A mechanism is described for facilitating storage management for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting one or more components associated with machine learning, where the one or more components include memory and a processor coupled to the memory, and where the processor includes a graphics processor. The method may further include allocating a storage portion of the memory and a hardware portion of the processor to a machine learning training set, where the storage and hardware portions are precise for implementation and processing of the training set.