Gyrfalcon Technology Inc.

Wenhan Zhang

Lin Yang

Baohua Sun

Xiang Gao

Patrick Dong

Jason Zeng Dong

Charles Jin Young

Chyu-Jiuh Torng

Daniel H Liu

Hualiang Yu

This disclosure relates to testing of integrated artificial intelligence (AI) circuit with embedded memory to improve effective chip yield and to mapping addressable memory segments of the embedded memory to multilayer AI networks at the network level, layer level, parameter level, and bit level based on bit error rate (BER) of the addressable memory segments. The disclosed methods and systems allows for deployment of one or more multilayer AI networks in an AI circuit with sufficient model accuracy even when the embedded memory has an overall BER higher than a preferred overall threshold.

Effective chip yield for artificial intelligence integrated circuit with embedded memory

Methods of encoding image data for loading into an artificial intelligence (AI) integrated circuit are provided. The AI integrated circuit may have an embedded cellular neural network for implementing AI tasks based on the loaded image data. An encoding method may apply image splitting, principal component analysis (PCA) or a combination thereof to an input image to generate a plurality of output images. Each output image has a size smaller than the size of the input image. The method may load the output images into the AI chip, execute programming instructions contained in the AI chip to generate an image recognition result based on the at least one of the plurality of output images, and output the image recognition result. The encoding method also trains a convolution neural network (CNN) and loads the weights of the CNN into the AI integrated circuit for implementing the AI tasks.

System and method for encoding data in an image/video recognition integrated circuit solution

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

Methods of encoding voice data for loading into an artificial intelligence (AI) integrated circuit are provided. The AI integrated circuit may have an embedded cellular neural network for implementing AI tasks based on the loaded voice data. An encoding method may generate a two-dimensional (2D) frequency-time array from an audio waveform, use the 2D frequency-time array to generate a set of 2D arrays to approximate the 2D frequency-time array, load the set of 2D arrays into the AI integrated circuit, execute programming instructions contained in the AI integrated circuit to feed the set of 2D arrays into the embedded cellular neural network in the AI integrated circuit to generate a voice recognition result, and output the voice recognition result. The encoding method also trains a convolution neural network (CNN) and loads the weights of the CNN into the AI integrated circuit for implementing the AI tasks.

System and method for encoding data in a voice recognition integrated circuit solution

Methods of encoding voice data for loading into an artificial intelligence (AI) integrated circuit are provided. The AI integrated circuit may have an embedded cellular neural network for implementing AI tasks based on the loaded voice data. An encoding method may generate a two-dimensional (2D) frequency-time array from an audio waveform, apply a probability function to the 2D frequency-time array to generate a set of 2D arrays, load the set of 2D arrays into the AI integrated circuit, execute programming instructions contained in the AI integrated circuit to feed the set of 2D arrays into the embedded cellular neural network in the AI integrated circuit to generate a voice recognition result, and output the voice recognition result. The encoding method also trains a convolution neural network (CNN) and loads the weights of the CNN into the AI integrated circuit for implementing the AI tasks.

Apparatus for recognition of handwritten Chinese characters contains a bus, an input means connecting to the bus for receiving input imagery data created from a handwritten Chinese character, a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit operatively connecting to the bus for extracting features out of the input imagery data using pre-trained filter coefficients of a plurality of order convolutional layers stored therein, a memory connecting the bus, the memory being configured for storing weight coefficients of fully-connected (FC) layers, a processing unit connecting to the bus for performing computations of FC layers to classify the extracted features from the CNN based integrated circuit to a particular Chinese character in a predefined Chinese character set, and a display unit connecting to the bus for displaying the particular Chinese character. Greater than 95% recognition accuracy is achieved using multiple bi-valued 3&#xd7;3 filter kernels as pre-trained filter coefficients.

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Mississauga, Canada

Wenhan Zhang