Object recognition with reduced neural network weight precision

Patent metadata

Field	Value
Publication number	US-10417525-B2
Application number	US-201514663233-A
Country	US
Kind code	B2
Filing date	Mar 19, 2015
Priority date	Sep 22, 2014
Publication date	Sep 17, 2019
Grant date	Sep 17, 2019

Abstract

A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

First claim

What is claimed is:

1. A client device configured with a trained neural network, the client device comprising:
a processor, a memory, a user interface, a communications interface, a power supply and an input device;
the memory comprising the trained neural network received from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network for the client device;
wherein:
the trained neural network is configured to generate a feature map, the feature map comprising a plurality of weight values derived from an input image; and
the trained neural network is configured to perform a unitary quantizing operation or a supervised iterative quantization operation on the feature map to reduce a number of bits of each weight of the plurality of weight values from a first predetermined number to a second predetermined number that is less than the first predetermined number without changing a dimension of the feature map.

2. The client device as in claim 1 , wherein the input device is configured to capture an image and to store image input data in the memory.

3. The client device as in claim 1 , further comprising a multilayer perceptron (MLP) classifier configured to map image input data.

4. The client device as in claim 1 , wherein the trained neural network comprises a convolutional neural network.

5. The client device as in claim 1 , wherein the quantization operation performs back-propagation (BP) of image input data.

6. The client device as in claim 1 , wherein the trained neural network is configured to perform object recognition.

7. The client device as in claim 1 , comprising one of a smartphone, a tablet computer and a portable electronic device.

8. The client device as in claim 1 , wherein the trained neural network is a quantized low-bit version of the server-based neural network.

9. The client device as in claim 1 , wherein network weights for the trained neural network are quantized for lower bit resolution by the server system.

10. A method that comprises performing the following using a client device:
receiving a trained neural network from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network for the client device;
capturing an input image;
processing the input image using the trained neural network;
generating a feature map using the trained neural network, the feature map comprising a plurality of weight values derived from an input image;
performing a unitary quantizing operation or a supervised iterative quantization operation on the feature map using the trained neural network to reduce a number of bits of each weight of the plurality of weight values from a first predetermined number to a second predetermined number that is less than the first predetermined number without changing a dimension of the feature map; and
recognizing an object in the input image based on a result of the processing.

11. The method of claim 10 , wherein receiving the trained neural network includes:
receiving a quantized low-bit version of the server-based neural network as the trained neural network.

12. The method of claim 11 , wherein receiving the quantized low-bit version includes:
receiving network weights associated with the trained neural network, wherein the network weights are quantized for lower bit resolution by the server system.

13. The method of claim 10 , wherein recognizing the object includes:
analyzing the result of the processing using a multilayer perceptron (MLP) classifier to recognize the object in the input image.

14. A non-transitory computer-readable medium storing program code, which, when executed by a processor, cause the processor to perform the following:
receive a trained neural network from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network;
capture an input image;
generating a feature map using the trained neural network, the feature map comprising a plurality of weight values derived from an input image;
performing a unitary quantizing operation or supervised iterative quantization operation on the feature map using the trained neural network to reduce a number of bits of each weight of the plurality of weight values from a first predetermined number to a second predetermined number that is less than the first predetermined number without changing a dimension of the feature map; and
process the input image using the trained neural network to recognize an object in the input image.

15. The non-transitory computer-readable medium of claim 14 , wherein the program code, when executed by the processor, cause the processor to further perform the following:
receive network weights associated with the trained neural network, wherein the network weights are quantized for lower bit resolution by the server system.

16. The non-transitory computer-readable medium of claim 14 , wherein the program code, when executed by the processor, cause the processor to further perform the following:
importing a low-resolution configuration of the server-based neural network; and
storing the imported configuration as the trained neural network.

17. A client device configured with a trained neural network, the client device comprising:
a processor, a memory, a user interface, a communications interface, a power supply and an input device;
the memory comprising the trained neural network received from a server system, wherein the server system has trained and configured a server-based neural network to be used as the trained neural network for the client device;
wherein:
the trained neural network is configured to generate a feature map, the feature map comprising a plurality of first weight values derived from an input image;
the trained neural network is configured to convert the first weights of the feature map into second weights by a unitary or a supervised iterative quantizing operation; and
the second weights are encoded using a number of bits lower than that used to encode the first weights without changing a dimension of the feature map.

Assignees

• Ji Zhengping
• Ovsiannikov Ilia
• Wang Yibing Michelle
• Shi Lilong
• Samsung Electronics Co Ltd

Inventors

• Ji Zhengping
• Ovsiannikov Ilia
• Wang Yibing Michelle
• Shi Lilong

Classifications

• G06V30/19127

  Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods · CPC title

• G06V30/18057

  Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN] · CPC title

• G06V30/19173

  Classification techniques · CPC title

• G06V10/7715

  Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods · CPC title

• G06N3/045

  Combinations of networks · CPC title