Gyrfalcon Technology Inc.

Patrick Dong

Lin Yang

Baohua Sun

Jason Zeng Dong

Charles Jin Young

Yequn Zhang

Wenhan Zhang

Michael Lin

Yongxiong Ren

Xiaochun Li

Bin Yang

Yinbo Shi

Dan Bin Liu

Catherine Chi

In some embodiments, a system includes an artificial intelligence (AI) chip and a processor coupled to the AI chip and configured to receive an input image, crop the input image into a plurality of cropped images, and execute the AI chip to produce a plurality of feature maps based on at least a subset of the plurality of cropped images. The system may further merge at least a subset of the plurality of feature maps to form a merged feature map, and produce an output image based on the merged feature map. The cropping and merging operations may be performed according to a same pattern. The system may also include a training network configured to train weights of the CNN model in the AI chip in a gradient descent network. Cropping and merging may be performed over the training sample images in the training work in a similar manner.

Combining feature maps in an artificial intelligence semiconductor solution

A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

System and method for determining an artificial intelligence model in a decentralized network

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.

Ensemble learning based image classification systems

Two-dimensional symbols with each containing multiple ideograms for facilitating machine learning are disclosed. Two-dimensional symbol comprises a matrix of N&#xd7;N pixels of data representing a 'super-character'. The matrix is divided into M&#xd7;M sub-matrices with each of the sub-matrices containing (N/M)&#xd7;(N/M) pixels. N and M are positive integers or whole numbers, and N is preferably a multiple of M. Each of the sub-matrices represents one ideogram defined in an ideogram collection set. 'Super-character' represents at least one meaning each formed with a specific combination of a plurality of ideograms. Ideogram collection set includes, but is not limited to, pictograms, logosyllabic characters, Japanese characters, Korean characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters. Features of each ideogram can be represented by more than one layer of two-dimensional symbol.

Two-dimensional symbol for facilitating machine learning of combined meaning of multiple ideograms contained therein

A local processing device contains a bus, an input interface and at least one cellular neural networks (CNN) based integrated circuit (IC). The input interface is receiving a 2-D symbol representing a Chinese poetry or verse. The 2-D symbol is a matrix of N&#xd7;N pixels of K-bit data that contains a 'super-character'. The matrix is divided into M&#xd7;M sub-matrices each containing (N/M)&#xd7;(N/M) pixels. Each of the sub-matrices represents an ideogram. K, N and M are positive integers, and N is a multiple of M. CNN based IC is configured for understanding semantic meaning of the Chinese poetry or verse within the 'super-character' contained in the 2-D symbol. The ideogram is created by embedded fonts of all of the characters contained in a corresponding phrase of the Chinese poetry or verse or is a pictogram representing artistic conception of each sentence of the Chinese poetry or verse.

Deep learning device for local processing classical chinese poetry and verse

Image classification system contains a CNN based IC configured for extracting features out of input data by performing convolution operations using filter coefficients of ordered convolutional layers and a classifier IC configured for classifying the input data using reduced set of the extracted features based on a light-weight classifier. Light-weight classifier is derived by: training filter coefficients of the ordered convolutional layers using a dataset containing N labeled data, the trained filter coefficients are for the CNN based IC; outputting respective extracted features of the N labeled data after performing convolution operations of ordered convolutional layers using the trained filter coefficients, each labeled data contains X features; creating the reduced set of the extracted features by eliminating those of the X features that contain zeros in at least M of the N labeled data; and adjusting M until the light-weight classifier achieves satisfactory results using the reduced set.

Image classification systems based on CNN based IC and light-weight classifier

A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.

Object detection and recognition apparatus based on CNN based integrated circuits

Multiple 3&#xd7;3 convolutional filter kernels are used for approximating operations of fully-connected (FC) layers. Image classification task is entirely performed within a CNN based integrated circuit. Output at the end of ordered convolutional layers contains P feature maps with F&#xd7;F pixels of data per feature map. 3&#xd7;3 filter kernels comprises L layers with each organized in an array of R&#xd7;Q of 3&#xd7;3 filter kernels, Q and R are respective numbers of input and output feature maps of a particular layer of the L layers. Each input feature map of the particular layer comprises F&#xd7;F pixels of data with one-pixel padding added around its perimeter. Each output feature map of the particular layer comprises (F&#x2212;2)&#xd7;(F&#x2212;2) pixels of useful data. Output of the last layer of the L layers contains Z classes. L equals to (F&#x2212;1)/2 if F is an odd number. P, F, Q, R and Z are positive integers.

Approximating fully-connected layers with multiple arrays of 3x3 convolutional filter kernels in a CNN based integrated circuit

CNN based integrated circuit is configured with a set of pre-trained filter coefficients or weights as a feature extractor of an input data. Multiple fully-connected networks (FCNs) are trained for use in a hierarchical category classification scheme. Each FCN is capable of classifying the input data via the extracted features in a specific level of the hierarchical category classification scheme. First, a root level FCN is used for classifying the input data among a set of top level categories. Then, a relevant next level FCN is used in conjunction with the same extracted features for further classifying the input data among a set of subcategories to the most probable category identified using the previous level FCN. Hierarchical category classification scheme continues for further detailed subcategories if desired.

Hierarchical category classification scheme using multiple sets of fully-connected networks with a CNN based integrated circuit as feature extractor

Method and systems of replacing operations of depthwise separable filters with first and second replacement convolutional layers are disclosed. Depthwise separable filters contains a combination of a depthwise convolutional layer followed by a pointwise convolutional layer with input of P feature maps and output of Q feature maps. The first replacement convolutional layer contains P&#xd7;P of 3&#xd7;3 filter kernels formed by placing each of the P&#xd7;1 of 3&#xd7;3 filter kernels of the depthwise convolutional layer on respective P diagonal locations, and zero-value 3&#xd7;3 filter kernels zero-value 3&#xd7;3 filter kernels in all off-diagonal locations. The second replacement convolutional layer contains Q&#xd7;P of 3&#xd7;3 filter kernels formed by placing Q&#xd7;P of 1&#xd7;1 filter coefficients of the pointwise convolutional layer in center position of the respective Q&#xd7;P of 3&#xd7;3 filter kernels, and numerical value zero in eight perimeter positions.

Implementation of MobileNet in a CNN based digital integrated circuit

Operations of a combination of first and second original convolutional layers followed by a short path are replaced by operations of a set of three particular convolutional layers. The first contains 2N&#xd7;N filter kernels formed by placing said N&#xd7;N filter kernels of the first original convolutional layer in left side and N&#xd7;N filter kernels of an identity-value convolutional layer in right side. The second contains 2N&#xd7;2N filter kernels formed by placing the N&#xd7;N filter kernels of the second original convolutional layer in upper left corner, N&#xd7;N filter kernels of an identity-value convolutional layer in lower right corner, and N&#xd7;N filter kernels of two zero-value convolutional layers in either off-diagonal corner. The third contains N&#xd7;2N of kernels formed by placing N&#xd7;N filter kernels of a first identity-value convolutional layer and N&#xd7;N filter kernels of a second identity-value convolutional layer in a vertical stack. Each filter kernel contains 3&#xd7;3 filter coefficients.

Implementation of ResNet in a CNN based digital integrated circuit

An artificial intelligence inference computing device contains a printed circuit board (PCB) and a number of electronic components mounted thereon. Electronic components include a wireless communication module, a controller module, a memory module, a storage module and at least one cellular neural networks (CNN) based integrated circuit (IC) configured for performing convolutional operations in a deep learning model for extracting features out of input data. Each CNN based IC includes a number of CNN processing engines operatively coupled to at least one input/output data bus. CNN processing engines are connected in a loop with a clock-skew circuit. Wireless communication module is configured for transmitting pre-trained filter coefficients of the deep learning model, input data and classification results.

Artificial intelligence inference computing device

Methods of recognizing motions of an object in a video clip or an image sequence are disclosed. A plurality of frames are selected out of a video clip or an image sequence of interest. A text category is associated with each frame by applying an image classification technique with a trained deep-learning model for a set of categories containing various poses of an object within each frame. A 'super-character' is formed by embedding respective text categories of the frames as corresponding ideograms in a 2-D symbol having multiple ideograms contained therein. Particular motion of the object is recognized by obtaining the meaning of the 'super-character' with image classification of the 2-D symbol via a trained convolutional neural networks model for various motions of the object derived from specific sequential combinations of text categories. Ideograms may contain imagery data instead of text categories, e.g., detailed images or reduced-size images.

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San Jose, CA, United States of America

Patrick Dong