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.

Date of Patent:
Jun. 13, 2023

Filed:

Apr. 03, 2018
Applicant:

Hailo Technologies Ltd., Tel-Aviv, IL;

Inventors:

Avi Baum, Givat Shmuel, IL;

Or Danon, Kiryat Ono, IL;

Hadar Zeitlin, Kfar Saba, IL;

Daniel Ciubotariu, Ashdod, IL;

Rami Feig, Zofit, IL;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 12/02 (2006.01); G06N 3/063 (2023.01); G06F 12/06 (2006.01); G06N 20/00 (2019.01); G06F 30/30 (2020.01); G06F 30/27 (2020.01); G06F 18/00 (2023.01); G06N 3/045 (2023.01); G06F 7/501 (2006.01); G06F 7/523 (2006.01); G06F 9/50 (2006.01); G06F 17/10 (2006.01); G06F 5/01 (2006.01); G06N 3/08 (2023.01); G06F 13/16 (2006.01); G06N 3/04 (2023.01); G06F 9/30 (2018.01); G06N 3/084 (2023.01); G06N 3/02 (2006.01); G06N 3/082 (2023.01);
U.S. Cl.
CPC ...
G06F 12/0207 (2013.01); G06F 5/01 (2013.01); G06F 7/501 (2013.01); G06F 7/523 (2013.01); G06F 9/30054 (2013.01); G06F 9/5016 (2013.01); G06F 9/5027 (2013.01); G06F 12/02 (2013.01); G06F 12/0646 (2013.01); G06F 12/0692 (2013.01); G06F 13/1663 (2013.01); G06F 17/10 (2013.01); G06F 18/00 (2023.01); G06F 30/27 (2020.01); G06F 30/30 (2020.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G06N 3/082 (2013.01); Y02D 10/00 (2018.01);
Abstract

A novel and useful neural network (NN) processing core incorporating inter-device connectivity and adapted to implement artificial neural networks (ANNs). A chip-to-chip interface spreads a given ANN model across multiple devices in a seamless manner. The NN processor is constructed from self-contained computational units organized in a hierarchical architecture. The homogeneity enables simpler management and control of similar computational units, aggregated in multiple levels of hierarchy. Computational units are designed with minimal overhead as possible, where additional features and capabilities are aggregated at higher levels in the hierarchy. On-chip memory provides storage for content inherently required for basic operation at a particular hierarchy and is coupled with the computational resources in an optimal ratio. Lean control provides just enough signaling to manage only the operations required at a particular hierarchical level. Dynamic resource assignment agility is provided which can be adjusted as required depending on resource availability and capacity of the device.


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