Gyrfalcon Technology Inc.

Vlsi Technology, Inc.

Legend Silicon Corporation

Headway Technologies, Incorporated

Applied Spintronics, Inc.

Cadence Design Systems, Inc.

Other

Fitbit, Inc.

Cadonca Design Systems, Inc.

Lin Yang

Baohua Sun

Patrick Dong

Qi Dong

Chyu-Jiuh Torng

Jason Zeng Dong

Charles Jin Young

Yequn Zhang

Wenhan Zhang

Chun-Ling Liu

Yan Zhong

Yongxiong Ren

Xiang Gao

Qin Liu

Liubo Hong

Tom Zhong

Daniel H Liu

Manouchehr S Rafie

Xiaochun Li

Bin Yang

Zhi-Xing Yang

Kazuyoshi Moriya

Chun L Liu

Michael Lin

Henry K Jun

Huihua Yu

Conor Joseph Heneghan

Mao Yu

Gibong Jeong

Andrew Larsen Axley

Lindsey Michelle Sunden

Chris Hanrahan Sarantos

Jeffrey Andrew Fisher

Thomas Samuel Elliot

Nicholas Myers

Man-Chi Liu

Lukas Bielskis

Jaydip Das

Jose Roberto Melgoza

Syang-Myau Hwang

Dinesh Venkatachalam

Kevin Hwang

Yinbo Shi

Ke Gong

Yi Fan

Priya Vijay Sheth

Benjamin B Perkins

Hao Sha

Juan Ignacio Correa Ramírez

Samuel Barry Tellman

Dan Bin Liu

Catherine Chi

Sleep tracking systems and techniques for monitoring two or more co-sleepers in a single bed are disclosed. Such systems and techniques may incorporate sleeper identification, as well as various non-user-specific aspects. Some implementations may incorporate user-specific or user-tailored alarm functionality.

Sleep monitoring system with optional alarm functionality

A pixel feature vector extraction system for extracting multi-scale features contains a cellular neural networks (CNN) based integrated circuit (IC) for extracting pixel feature vector out of input imagery data by performing convolution operations using pre-trained filter coefficients of ordered convolutional layers in a deep learning model. The ordered convolutional layers are organized in a number of groups with each group followed by a pooling layer. Each group is configured for a different size of feature map. Pixel feature vector contains a combination of feature maps from at least two groups, for example, concatenation of the feature maps. The first group of the at least two groups contains the largest size of the feature maps amongst all of the at least two groups. Feature maps of the remaining of the at least two groups are modified to match the size of the feature map of the first group.

Apparatus and methods of obtaining multi-scale feature vector using CNN based integrated circuits

A system for training an artificial intelligence (AI) model for an AI chip to implement an AI task may include an AI training unit to train weights of an AI model in floating point, a convolution quantization unit for quantizing the trained weights to a number of quantization levels, and an activation quantization unit for updating the weights of the AI model so that output of the AI model based at least on the updated weights are within a range of activation layers of the AI chip. The updated weights may be stored in fixed point and uploadable to the AI chip. The various units may be configured to account for the hardware constraints in the AI chip to minimize performance degradation when the trained weights are uploaded to the AI chip and expedite training convergence. Forward propagation and backward propagation may be combined in training the AI model.

Using quantization in training an artificial intelligence model in a semiconductor solution

A system may include multiple client devices and a processing device communicatively coupled to the client devices. Each client device includes an artificial intelligence (AI) chip and is configured to generate an AI model. The processing device may be configured to (i) receive a respective AI model and an associated performance value of the respective AI model from each of the plurality of client devices; (ii) determine an optimal AI model based on the performance values associated with the respective AI models from the plurality of client devices; and (iii) determine a global AI model based on the optimal AI model. The system may load the global AI model into an AI chip of a client device to cause the client device to perform an AI task based on the global AI model in the AI chip. The AI model may include a convolutional neural network.

Systems and methods for determining an artificial intelligence model in a communication system

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 device for obtaining a local optimal AI model may include an artificial intelligence (AI) chip and a processing device configured to receive a first initial AI model from the host device. The device may load the initial AI model into the AI chip to determine a performance value of the AI model based on a dataset, and determine a probability that a current AI model should be replaced by the initial AI model. The device may determine, based on the probability, whether to replace the current AI model with the initial AI model. If it is determined that the current AI model be replaced, the device may replace the current AI model with the initial AI model. The device may repeat the above processes and obtain a final current AI model. The device may transmit the final current AI model to the host device.

P feature encoding values are obtained for each of the Q frames in a video clip by image transformations of each frame along with performing computations of a specific succession of convolution and pooling layers of a CNN based deep learning model followed with operations of a nested invariance pooling layer. Each feature encoding value is then converted from real number to a corresponding integer value within a range designated for color display intensity according to a quantization scheme. A 2-D graphical symbol that contains N&#xd7;N pixels is formed by placing respective color display intensities into the N&#xd7;N pixels according to a data arrangement pattern for representing all frames of the video clip in form of P&#xd7;Q feature encoding values, such that the 2-D graphical symbol possesses a semantic meaning of the video clip that can be recognized via image classification task using another trained CNN based deep learning model.

2-D graphical symbols for representing semantic meaning of a video clip

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 image and the maximum number of tokens for a to-be-created image caption are received in a computing system. Font size of graphical image of the token is calculated from the maximum number of tokens and the dimension of desired input image for prediction-style image classification technique. Desired input image is divided into first and second portions. A 2-D symbol is formed by placing a resized image derived from the received image with substantially similar contents in the first portion and by initializing the second portion with blank images. Next token of the image caption is predicted by classifying the 2-D symbol using the prediction-style image classification technique. 2-D symbol is modified by appending the graphical image of just-predicted token to the existing image caption in the second portion, if termination condition for image caption creation is false. Next token is repeatedly predicted until termination condition becomes true.

Artificial intelligence based image caption creation systems and methods thereof

An integrated circuit includes an artificial intelligence (AI) logic and an embedded memory coupled to the AI logic and connectable to an external processor. The embedded memory includes multiple storage cells and multiple reference units. One or more reference units in the memory are selected for memory access through configuration at chip packaging level by the external processor. The external processor may execute a self-test process to select or update the one or more reference units for memory access so that the error rate of memory is below a threshold. The self-test process may be performed, via a memory initialization controller in the memory, to test and reuse the reference cells in the memory at chip level. The embedded memory may be a STT-MRAM, SOT, OST MRAM, and/or MeRAM memory.

Method of self-testing and reusing of reference cells in a memory architecture

CNN based digital IC for AI contains a number of CNN processing units. Each CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem having first and second memories. The first memory includes an array of magnetic random access memory (RAM) cells for storing weights (e.g., filter coefficients) and the second memory contains SRAM for storing input signals (e.g., imagery data). The first memory may store one-time-programming weights. The memory subsystem may contain a third memory that contains magnetic RAM cells for storing one-time-programming data for security purpose. The magnetic RAM includes STT-RAM or OST-MRAM in SLC or MLC technology.

Memory subsystem in CNN based digital IC for artificial intelligence

CNN based digital IC for AI contains a number of CNN processing units. A first CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem, which includes a first one-time-programming (OTP) memory for filter coefficients and a second memory for imagery data. A second CNN processing unit contains CNN logic circuits operatively coupling to a memory subsystem that includes a first memory for filter coefficients, a second memory for imagery data and a third OTP memory for unique data pattern (e.g., security purpose). Either STT-RAM or OST-MRAM can be configured as different memories of the memory subsystem.

Buffer memory architecture for a CNN based processing unit and creation methods thereof

Embedded memory subsystems in a digital integrated circuit for artificial intelligence are disclosed. A semi-conductor substrate contains CNN processing units. Each CNN processing unit includes CNN logic circuits and an embedded memory subsystem. The memory subsystem includes first embedded memory and second embedded memory. The first embedded memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area with a diameter in a range of 40-120 nm. The second embedded memory contains an array of MTJ STT-RAM cells with each cell has a circular planar area having a diameter in a range of 30-75 nm.

Embedded memory subsystems for a CNN based processing unit and methods of making

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

Growing community of inventors

Milpitas, CA, United States of America

Lin Yang