公开(公告)号：US10692003B2

公开(公告)日：2020-06-23

申请号：US16445330

申请日：2019-06-19

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US11803747B2

公开(公告)日：2023-10-31

申请号：US16878720

申请日：2020-05-20

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/10 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/105 ,  G06N5/04

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US20240062062A1

公开(公告)日：2024-02-22

申请号：US18376362

申请日：2023-10-03

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/10 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N5/04 ,  G06N3/105 ,  G06N3/044 ,  G06N3/045

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US20200279163A1

公开(公告)日：2020-09-03

申请号：US16878720

申请日：2020-05-20

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/04 ,  G06N3/10

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US20190303761A1

公开(公告)日：2019-10-03

申请号：US16445330

申请日：2019-06-19

Applicant: Google LLC

Inventor： Samy Bengio ,  Mohammad Edward Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.