Patent Application

App. No. 17/190,557

Abstract

Claims (44)

1 . A method for training a neural network, comprising:

determining a group of optimal network structures for a prunable neural network under various operation workloads based on a training data set; and

training the prunable neural network based on the training data set and the group of optimal network structures, such that the trained prunable neural network has, under a given operation workload, an optimal network structure corresponding to the given operation workload.

2 . The method according to claim 1 , wherein determining the group of optimal network structures comprises:

determining a group of candidate network structures for the prunable neural network under a first operation workload; and

selecting a candidate network structure with the best performance from the group of candidate network structures for use as an optimal network structure corresponding to the first operation workload.

3 . The method according to claim 2 , wherein determining the group of candidate network structures comprises:

determining a complete network structure for the prunable neural network under a maximum operation workload;

determining a group of compression modes usable for the complete network structure based on the first operation workload and the maximum operation workload; and

compressing the complete network structure based on the group of compression modes to determine the group of candidate network structures.

4 . The method according to claim 1 , wherein training the prunable neural network comprises iteratively executing following operations at least once:

determining an operation workload set for training the prunable neural network, the operation workload set comprising a maximum operation workload, a minimum operation workload, and an intermediate operation workload selected between the maximum operation workload and the minimum operation workload;

determining a first optimal network structure corresponding to the maximum operation workload, a second optimal network structure corresponding to the minimum operation workload, and a third optimal network structure corresponding to the intermediate operation workload from the group of optimal network structures;

training the prunable neural network based on the training data set and the first optimal network structure corresponding to the maximum operation workload; and

further training the prunable neural network based on the training data set, the second optimal network structure corresponding to the minimum operation workload, and the third optimal network structure corresponding to the intermediate operation workload.

5 . A method for deploying a neural network, comprising:

acquiring a trained prunable neural network, the prunable neural network being trained to have, under a given operation workload, an optimal network structure corresponding to the given operation workload;

determining, based on information and an expected performance related to a target device, a target operation workload to be applied to the target device; and

deploying the prunable neural network to the target device based on the target operation workload, the deployed prunable neural network having an optimal network structure corresponding to the target operation workload.

6 . The method according to claim 5 , wherein the expected performance comprises at least one of an expected accuracy and expected response time.

7 . An electronic device, comprising:

at least one processing unit; and

at least one memory, the at least one memory being coupled to the at least one processing unit and storing an instruction for execution by the at least one processing unit, the instruction, when executed by the at least one processing unit, causing the device to execute actions, the actions comprising:

determining a group of optimal network structures for a prunable neural network under various operation workloads based on a training data set; and

training the prunable neural network based on the training data set and the group of optimal network structures, such that the trained prunable neural network has, under a given operation workload, an optimal network structure corresponding to the given operation workload.

8 . The device according to claim 7 , wherein determining the group of optimal network structures comprises:

determining a group of candidate network structures for the prunable neural network under a first operation workload; and

selecting a candidate network structure with the best performance from the group of candidate network structures for use as an optimal network structure corresponding to the first operation workload.

9 . The device according to claim 8 , wherein determining the group of optimal network structures comprises:

determining a complete network structure for the prunable neural network under a maximum operation workload;

determining a group of compression modes usable for the complete network structure based on the first operation workload and the maximum operation workload; and

compressing the complete network structure based on the group of compression modes to determine the group of candidate network structures.

10 . The device according to claim 7 , wherein training the prunable neural network comprises iteratively executing following operations at least once:

determining an operation workload set for training the prunable neural network, the operation workload set comprising a maximum operation workload, a minimum operation workload, and an intermediate operation workload selected between the maximum operation workload and the minimum operation workload;

determining a first optimal network structure corresponding to the maximum operation workload, a second optimal network structure corresponding to the minimum operation workload, and a third optimal network structure corresponding to the intermediate operation workload from the group of optimal network structures;

training the prunable neural network based on the training data set and the first optimal network structure corresponding to the maximum operation workload; and

further training the prunable neural network based on the training data set, the second optimal network structure corresponding to the minimum operation workload, and the third optimal network structure corresponding to the intermediate operation workload.

11 . The device according to claim 7 , wherein the actions further comprise:

acquiring a trained prunable neural network, the prunable neural network being trained to have, under a given operation workload, an optimal network structure corresponding to the given operation workload;

determining, based on information and an expected performance related to a target device, a target operation workload to be applied to the target device; and

deploying the prunable neural network to the target device based on the target operation workload, the deployed prunable neural network having an optimal network structure corresponding to the target operation workload.

12 . The device according to claim 11 , wherein the expected performance comprises at least one of an expected accuracy and expected response time.

13 . A computer program product, the computer program product being tangibly stored on a non-transitory computer-readable medium and comprising a machine-executable instruction, the machine-executable instruction, when executed, causing a machine to execute steps of the method according to claim 1 .

14 . A computer program product, the computer program product being tangibly stored on a non-transitory computer-readable medium and comprising a machine-executable instruction, the machine-executable instruction, when executed, causing a machine to execute steps of the method according to claim 5 .

Assignments (10)