# US 10,482,374 B1 · Ensemble learning based image classification systems

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- Number: US 10,482,374 B1
- Document ID: US10482374B1
- Status: Granted
- Kind: B1
- Application number: 16/503,402
- Filed: 2019-07-03
- Granted: 2019-11-19
- Earliest claimed: 2018-10-22
- Assignees: Gyrfalcon Technology Inc.
- Applicants: Gyrfalcon Technology Inc.
- Inventors: Lin Yang; Patrick Z. Dong; Charles Jin Young; Jason Z. Dong; Michael Lin; Baohua Sun
- Web page: https://patentgrep.com/patent/US10482374B1

## CPC

- G06N 3/063: using electronic means
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines
- G06F 18/2413: based on distances to training or reference patterns
- G06F 18/254: {of classification results, e.g. of results related to same input data}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/0495: Quantised networks; Sparse networks; Compressed networks
- G06N 3/09: Supervised learning
- G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn
- G06N 7/00: Computing arrangements based on specific mathematical models
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/809: {of classification results, e.g. where the classifiers operate on the same input data}
- G06V 10/955: {using specific electronic processors}
- G06V 20/00: Scenes; Scene-specific elements (control of digital cameras H04N23/60)
- G06F 18/259: {Fusion by voting}
- G06N 3/08: Learning methods
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.

## Claims

### Claim 1

An ensemble learning based image classification system comprising:
a plurality of cellular neural networks (CNN) based integrated circuits (ICs) with one of the plurality of CNN based ICs configured as a meta learner of an ensemble for an image classification task while the remaining CNN based ICs operatively coupling together as a set of base learners, each base learner being configured with at least one of a plurality of distinct deep learning models;
a controller configured for managing data and computation flow of the ensemble; and
a memory based data buffer for holding input imagery data to be classified via the ensemble.

### Claim 2 (depends on 1)

The ensemble learning based image classification system of claim 1, wherein said each of the plurality of CNN based ICs further comprising:
a plurality of cellular neural networks (CNN) processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit, each CNN processing engine comprising:
a CNN processing block configured for simultaneously performing convolutional operations using the input imagery data and pre-trained filter coefficients of the at least one of the plurality of distinct deep learning models;
a first set of memory buffers operatively coupling to the CNN processing block for storing relevant portion of the input imagery data; and
a second set of memory buffers operative coupling to the CNN processing block for storing the pre-trained filter coefficients.

### Claim 3 (depends on 1)

The ensemble learning based image classification system of claim 1, wherein the set of base learners are configured for outputting respective feature vectors.

### Claim 4 (depends on 3)

The ensemble learning based image classification system of claim 3, wherein the respective feature vectors are concatenated together to form an image input for the meta learner.

### Claim 5 (depends on 1)

The ensemble learning based image classification system of claim 1, wherein the controller is further configured for performing computations of fully-connected layers.

### Claim 6 (depends on 1)

The ensemble learning based image classification system of claim 1, wherein the memory based data buffer comprises static random access memory.