Patents by Inventor Yen-Kuang Chen

Yen-Kuang Chen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11409839
    Abstract: The present disclosure relates to a method for controlling execution of a GEMM operation on an accelerator comprising multiple computation units, a first memory device, and a second memory device. The method comprises determining an execution manner of the GEMM operation, the execution manner comprising partition information of the GEMM operation and computation unit allocation information of the partitioned GEMM operation; generating one or more instructions to compute the partitioned GEMM operation on one or more allocated computation units; and issuing the one or more instructions to at least one of a first queue and a second queue, which enables at least one of a first local controller and a second local controller to execute the one or more instructions, wherein the first local controller and the second local controller are configured to control data movement between the computation units, the first memory device, and the second memory device.
    Type: Grant
    Filed: August 21, 2020
    Date of Patent: August 9, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Yuhao Wang, Fei Sun, Fei Xue, Yen-Kuang Chen, Hongzhong Zheng
  • Patent number: 11403782
    Abstract: Methods and systems are provided for implementing static channel filtering operations upon image datasets transformed to frequency domain representations, including decoding images of an image dataset to generate a frequency domain representation of the image dataset; discarding coefficient values of one or more particular frequency channels of each image of the image dataset in a frequency domain representation; and transporting the image dataset in a frequency domain representation to one or more special-purpose processor(s). Methods and systems of the present disclosure may enable a filtered image dataset to be input to a second layer of a learning model, bypassing a first layer, or may enable a learning model to be designed with a reduced-size first layer. This may achieve benefits such as reducing computational overhead and time of machine learning training and inference computations, reducing volume of image data input into the learning model, and reducing convergence time.
    Type: Grant
    Filed: December 31, 2019
    Date of Patent: August 2, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Kai Xu, Fei Sun, Minghai Qin, Yen-kuang Chen
  • Patent number: 11403783
    Abstract: A system for processing encoded image components for artificial intelligence tasks. The system can include one or more compute units, one or more controllers and memory. The one or more controllers can include one or more micro-op schedulers and one or more channel switches. The one or more compute units can be configured to process components of the transformed domain image data according to one or more micro-operations for an artificial intelligence task. The one or more channel switches can be configured to selectively control the transfer of the components of transformed domain image data to the one or more compute units based on one or more gating flags. The one or more channel switches can also be configured to selectively control generation of the one or more micro-operations by the one or more micro-op schedulers based on the one or more gating flags.
    Type: Grant
    Filed: November 14, 2019
    Date of Patent: August 2, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Kai Xu, Minghai Qin, Yuhao Wang, Fei Sun, Yen-kuang Chen, Yuan Xie
  • Patent number: 11403090
    Abstract: This application describes methods, systems, and apparatus, including computer programs encoded on computer storage media, of an AI-assisted compiler. An example method includes obtaining intermediate code and executable code generated by compiling a computer program with a compiler; determining a reward based on one or more traces obtained by executing the executable code in a runtime system; generating an embedding vector based on the intermediate code and the one or more traces to represent code execution states; determining, using a reinforcement learning agent, one or more optimization actions based on the embedding vector and the reward; and updating the compiler by applying the one or more optimization actions.
    Type: Grant
    Filed: December 8, 2020
    Date of Patent: August 2, 2022
    Assignee: ALIBABA GROUP HOLDING LIMITED
    Inventors: Yuanwei Fang, Yen-kuang Chen
  • Publication number: 20220223035
    Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.
    Type: Application
    Filed: October 8, 2021
    Publication date: July 14, 2022
    Applicant: Intel Corporation
    Inventors: Shao-Wen Yang, Eve M. Schooler, Maruti Gupta Hyde, Hassnaa Moustafa, Katalin Klara Bartfai-Walcott, Yen-Kuang Chen, Jessica McCarthy, Christina R. Strong, Arun Raghunath, Deepak S. Vembar
  • Publication number: 20220223536
    Abstract: Semiconductor structures and method of forming the same are provided. A semiconductor structure according to the present disclosure includes a contact feature in a dielectric layer, a passivation structure over the dielectric layer, a conductive feature over the passivation structure, a seed layer disposed between the conductive feature and the passivation structure, a protecting layer disposed along sidewalls of the conductive feature, and a passivation layer over the conductive feature and the protecting layer.
    Type: Application
    Filed: May 5, 2021
    Publication date: July 14, 2022
    Inventors: Wen-Chun Wang, Tzy-Kuang Lee, Chih-Hsien Lin, Ching-Hung Kao, Yen-Yu Chen
  • Patent number: 11386873
    Abstract: A method for of encoding an application screen comprises partitioning graphic data into a plurality of graphic layers and classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. The method further comprises classifying each of the plurality of graphic layers as either a screen content (SC) or a non-screen content (non-SC) layer. Further, the method comprises rendering and encoding the one or more SC layers using a first codec and the one or more non-SC layers using a second codec.
    Type: Grant
    Filed: April 1, 2020
    Date of Patent: July 12, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Tae Meon Bae, Sicheng Li, Yen-kuang Chen, Guanlin Wu, Shaolin Xie, Minghai Qin, Qinggang Zhou
  • Patent number: 11388423
    Abstract: A video processing unit can include a non-object-based region-of-interest detection neural network, a threshold selection module and a region-of-interest map generator. The non-object-based region-of-interest detection neural network can be configured to receive a video frame and generate a plurality of candidate non-object-based region-of-interest blocks. The threshold selection module can be configured to receive the plurality of candidate non-object-based region-of-interest blocks and identify a plurality of selected region-of-interest blocks based on a predetermined threshold. The region-of-interest map generator can be configured to receive the selected non-object-based region-of-interest blocks and generate a region-of-interest map.
    Type: Grant
    Filed: March 23, 2020
    Date of Patent: July 12, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Minghai Qin, Sicheng Li, Guanlin Wu, Tae Meon Bae, Yen-kuang Chen
  • Publication number: 20220215241
    Abstract: This application describes methods, systems, and apparatus, including computer programs encoded on computer storage media, for microarchitecture-aware program sampling. An exemplary method includes receiving one or more traces collected from one or more microarchitectures executing a computer program for evaluating hardware configurations; training a machine learning (ML) model with multi-task learning based on the one or more traces as one or more training tasks; generating a plurality of embedded vectors representing the computer program; and updating, based on the trained ML model, the plurality of embedded vectors.
    Type: Application
    Filed: January 5, 2021
    Publication date: July 7, 2022
    Inventors: Yuanwei FANG, Minghai QIN, Yen-kuang CHEN
  • Publication number: 20220210008
    Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.
    Type: Application
    Filed: August 6, 2021
    Publication date: June 30, 2022
    Applicant: Intel Corporation
    Inventors: Shao-Wen Yang, Michael J. Nolan, Ignacio J. Alvarez Martinez, Robert Adams, John Brady, Mark Kelly, Yen-Kuang Chen
  • Patent number: 11375241
    Abstract: The present disclosure relates to a method for compensating an image. The method comprises estimating transform coefficients of a frequency component for a first image based on the first image, performing a dot multiplication operation between the estimated transform coefficients and a basis function associated with the frequency component to generate a compensation image, and combining the first image and the compensation image to generate a combined image.
    Type: Grant
    Filed: October 20, 2020
    Date of Patent: June 28, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Zihao Liu, Sicheng Li, Minghai Qin, Yen-kuang Chen
  • Patent number: 11366979
    Abstract: Image data is accessed. The image data includes frequency domain components. A subset of the frequency domain components is selected based on the relative importance of the frequency domain components. Only the subset of the frequency domain components is provided to an accelerator that executes a neural network to perform an artificial intelligence task using the subset of frequency domain components.
    Type: Grant
    Filed: November 14, 2019
    Date of Patent: June 21, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Yuhao Wang, Minghai Qin, Yen-Kuang Chen
  • Publication number: 20220191537
    Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.
    Type: Application
    Filed: October 25, 2021
    Publication date: June 16, 2022
    Applicant: Intel Corporation
    Inventors: Yiting Liao, Yen-Kuang Chen, Shao-Wen Yang, Vallabhajosyula S. Somayazulu, Srenivas Varadarajan, Omesh Tickoo, Ibrahima J. Ndiour
  • Patent number: 11360906
    Abstract: The devices within an inter-device processing system maintain data coherency in the last level caches of the system as a cache line of data is shared between the devices by utilizing a directory in one of the devices that tracks the coherency protocol states of the memory addresses in the last level caches of the system.
    Type: Grant
    Filed: August 14, 2020
    Date of Patent: June 14, 2022
    Assignee: Alibaba Group Holding Limited
    Inventors: Lide Duan, Hongyu Liu, Hongzhong Zheng, Yen-Kuang Chen
  • Publication number: 20220179635
    Abstract: This application describes methods, systems, and apparatus, including computer programs encoded on computer storage media, of an AI-assisted compiler. An example method includes obtaining intermediate code and executable code generated by compiling a computer program with a compiler; determining a reward based on one or more traces obtained by executing the executable code in a runtime system; generating an embedding vector based on the intermediate code and the one or more traces to represent code execution states; determining, using a reinforcement learning agent, one or more optimization actions based on the embedding vector and the reward; and updating the compiler by applying the one or more optimization actions.
    Type: Application
    Filed: December 8, 2020
    Publication date: June 9, 2022
    Inventors: Yuanwei FANG, Yen-kuang CHEN
  • Publication number: 20220173987
    Abstract: Example task assignment methods disclosed herein for video analytics processing in a cloud computing environment include determining a graph, such as a directed acyclic graph, including nodes and edges to represent a plurality of video sources, a cloud computing platform, and a plurality of intermediate network devices in the cloud computing environment. Disclosed example task assignment methods also include specifying task orderings for respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources.
    Type: Application
    Filed: September 13, 2021
    Publication date: June 2, 2022
    Inventors: Hong-Min Chu, Shao-Wen Yang, Yen-Kuang Chen
  • Publication number: 20220147567
    Abstract: A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.
    Type: Application
    Filed: January 21, 2022
    Publication date: May 12, 2022
    Inventors: Shaolin XIE, Minghai QIN, Yen-kuang CHEN, Tae Meon BAE, Qinggang ZHOU
  • Publication number: 20220124375
    Abstract: The present disclosure relates to a method for compensating an image. The method comprises estimating transform coefficients of a frequency component for a first image based on the first image, performing a dot multiplication operation between the estimated transform coefficients and a basis function associated with the frequency component to generate a compensation image, and combining the first image and the compensation image to generate a combined image.
    Type: Application
    Filed: October 20, 2020
    Publication date: April 21, 2022
    Inventors: Zihao Liu, Sicheng Li, Minghai Qin, Yen-kuang Chen
  • Publication number: 20220103831
    Abstract: The present disclosure relates to a method for scheduling computation resources for generating feature maps for video. The method comprises determining runtime for generating feature maps of a reference picture and a predicted picture, determining available computation resources for generating the feature maps, and allocating, based on the runtime, one or more computation resources among the available computation resources for generating the feature maps such that the feature maps are generated at regular time intervals.
    Type: Application
    Filed: September 30, 2020
    Publication date: March 31, 2022
    Inventors: Sicheng Ll, Yuanwei Fang, Minghai Qin, Yen-kuang Chen
  • Publication number: 20220094961
    Abstract: The systems and methods are configured to efficiently and effectively determine or find an estimated optimal encoding parameter set. In one embodiment, a video encoding parameter set estimation method comprises: performing an offline encoding parameter set characteristic prediction process that determines an estimate of a candidate encoding parameter set characteristic; and performing an encoding parameter set search process that identifies a predicted or estimated optimal video encoding parameter set. The encoding parameter set search process can include applying a constraint to the candidate encoding parameter set characteristic; and determining if candidate encoding parameter set meets an objective, wherein the determining is performed if the constraint is satisfied. The candidate encoding parameter set characteristic can be an estimated encoding time of the candidate encoding parameter set. The objective can be the best video quality out of a plurality of candidate encoding parameter sets.
    Type: Application
    Filed: May 4, 2021
    Publication date: March 24, 2022
    Inventors: Tae Meon BAE, Minghai QIN, Yen-kuang CHEN, Guanlin WU, Sicheng LI