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Bike Xie

from San Diego, CA
Age ~41
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Connected to:
Aihua H Xie, 63Alice Xie, 30Angela Xie, 65Anita Xie, 37April M Xie, 64Betty L Xie, 59Calvin Xie, 46Cathy Z Xie, 34Chengyu Xie, 44Claire S Xie, 39

Bike Xie Phones & Addresses

  • 13061 Signature Pt APT 222, San Diego, CA 92130
  • San Jose, CA
  • Sunnyvale, CA
  • Los Angeles, CA

Work

Company: Kneron Feb 2019 Position: Vice president of engineering

Education

Degree: Master of Science, Doctorates, Masters, Doctor of Philosophy School / High School: University of California, Los Angeles 2005 to 2010 Specialities: Electrical Engineering, Philosophy

Skills

Signal Processing • Matlab • Digital Signal Processors • Algorithms • Verilog • Ic • Simulations • Fpga • Embedded Systems • Vlsi • Soc • Asic • Circuit Design • Semiconductors • Eda

Industries

Semiconductors

Resumes

Resumes

Bike Xie Photo 1

Vice President Of Engineering

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Location:
13061 Signature Pt, San Diego, CA 92130
Industry:
Semiconductors
Work:
Kneron
Vice President of Engineering
Marvell Semiconductor
Senior Staff System Engineer
Marvell Semiconductor Apr 1, 2010 - Apr 2012
Staff System Engineer
Marvell Semiconductor Apr 2010 - Apr 2011
Senior System Engineer
Google Jun 2008 - Sep 2008
Intern Software Engineer
Education:
University of California, Los Angeles 2005 - 2010
Master of Science, Doctorates, Masters, Doctor of Philosophy, Electrical Engineering, Philosophy Tsinghua University 2001 - 2005
Bachelors, Bachelor of Science, Electrical Engineering University of California
Skills:
Signal Processing
Matlab
Digital Signal Processors
Algorithms
Verilog
Ic
Simulations
Fpga
Embedded Systems
Vlsi
Soc
Asic
Circuit Design
Semiconductors
Eda

Publications

Us Patents

Touchscreen System

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US Patent:
20130057506, Mar 7, 2013
Filed:
Jul 24, 2012
Appl. No.:
13/556833
Inventors:
Zixia Hu - Sunnyvale CA, US
Songping Wu - Cupertino CA, US
Bike Xie - Sunnyvale CA, US
Lun Dong - Sunnyvale CA, US
International Classification:
G06F 3/041
G06F 3/044
US Classification:
345174, 345173
Abstract:
This disclosure describes systems and techniques for implementing a touchscreen. These systems and/or techniques enable processing of a signal generated from one or more sensors of a touchscreen to reduce noise and increase accuracy.

Method Of Training Artificial Neural Network Using Sparse Connectivity Learning

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US Patent:
20200372363, Nov 26, 2020
Filed:
Jan 19, 2020
Appl. No.:
16/746941
Inventors:
- Taipei City, TW
Bike Xie - San Diego CA, US
YIYU ZHU - Nantong City, CN
International Classification:
G06N 3/08
G06N 3/063
G06N 5/04
G06N 20/00
Abstract:
A computing network includes a plurality of processing nodes. A method of training the computing network includes a processing node in the plurality of processing nodes computing an output estimate according to a weight defined by a weight variable and a connectivity mask, and adjusting connectivity variables according to an objective function to reduce a total number of connections between the plurality of processing nodes and reduce a performance loss indicative of how different the output estimate is from a target value. The connectivity mask represents a connection between the processing node and a preceding processing node in the plurality of processing nodes and is derived from a connectivity variable.

Low Precision And Coarse-To-Fine Dynamic Fixed-Point Quantization Design In Convolution Neural Network

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US Patent:
20200193270, Jun 18, 2020
Filed:
Aug 27, 2019
Appl. No.:
16/551753
Inventors:
- Taipei City, TW
YUNHAN MA - San Diego CA, US
Bike Xie - San Diego CA, US
Hsiang-Tsun Li - Taichung City, TW
JUNJIE SU - San Diego CA, US
Chun-Chen Liu - San Diego CA, US
International Classification:
G06N 3/04
G06N 3/08
Abstract:
After inputting input data to a floating pre-trained convolution neural network to generate floating feature maps for each layer of the floating pre-trained CNN model, a statistical analysis on the floating feature maps is performed to generate a dynamic quantization range for each layer of the floating pre-trained CNN model. Based on the obtained quantization range for each layer, the proposed quantization methodologies quantize the floating pre-trained CNN model to generate the scalar factor of each layer and the fractional bit-width of a quantized CNN model. It enables the inference engine performs low-precision fixed-point arithmetic operations to generate a fixed-point inferred CNN model.

Deep Neural Network With Low-Precision Dynamic Fixed-Point In Reconfigurable Hardware Design

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US Patent:
20200097816, Mar 26, 2020
Filed:
Aug 27, 2019
Appl. No.:
16/551743
Inventors:
- Taipei City, TW
Bike Xie - San Diego CA, US
Hsiang-Tsun Li - Taichung City, TW
Junjie Su - San Diego CA, US
Chun-Chen Liu - San Diego CA, US
International Classification:
G06N 3/08
G06N 3/063
G06F 7/544
Abstract:
A system for operating a floating-to-fixed arithmetic framework includes a floating-to-fix arithmetic framework on an arithmetic operating hardware such as a central processing unit (CPU) for computing a floating pre-trained convolution neural network (CNN) model to a dynamic fixed-point CNN model. The dynamic fixed-point CNN model is capable of implementing a high performance convolution neural network (CNN) on a resource limited embedded system such as mobile phone or video cameras.

Face Recognition Module With Artificial Intelligence Models

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US Patent:
20200082160, Mar 12, 2020
Filed:
Aug 1, 2019
Appl. No.:
16/528642
Inventors:
- Taipei City, TW
Bike Xie - San Diego CA, US
JUNJIE SU - San Diego CA, US
International Classification:
G06K 9/00
G01N 21/359
G06N 3/02
Abstract:
A face recognition module includes a near infrared flash, a master near infrared camera, an artificial intelligence NIR image model, an artificial intelligence original image model, and an artificial intelligence fusion model. The NIR flash flashes near infrared light. The master near infrared camera captures a NIR image. The artificial intelligence NIR image model processes the NIR image to generate NIR features. The artificial intelligence original image model processes a 2 dimensional second camera image to generate face features or color features. The artificial intelligence fusion model generates 3 dimensional face features, a depth map and an object's 3 dimensional model according to the NIR features, the face features and the color features.

Self-Tuning Model Compression Methodology For Reconfiguring Deep Neural Network And Electronic Device

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US Patent:
20190378013, Dec 12, 2019
Filed:
Jun 6, 2018
Appl. No.:
16/001923
Inventors:
- San Diego CA, US
JUNJIE SU - San Diego CA, US
Bike Xie - San Diego CA, US
Chun-Chen Liu - San Diego CA, US
International Classification:
G06N 3/08
G06N 3/04
G06N 3/063
Abstract:
A self-tuning model compression methodology for reconfiguring a Deep Neural Network includes: receiving a DNN model and a data set, wherein the DNN includes an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the DNN model includes a plurality of neurons; compressing the DNN model into a reconfigured model according to the data set, wherein the reconfigured model includes an input layer, at least one hidden layer and an output layer, and said at least one hidden layer and the output layer of the reconfigured model includes a plurality of neurons, and a size of the reconfigured model is smaller than a size of the DNN model; and executing the reconfigured model on a user terminal for an end-user application.

Systems And Methods For Capacitive Touch Detection

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US Patent:
20150324035, Nov 12, 2015
Filed:
May 8, 2015
Appl. No.:
14/707694
Inventors:
- St. Michael, BB
Hao Zhou - Shanghai, CN
Bike Xie - San Jose CA, US
Kanke Gao - Fremont CA, US
Xudong Shen - Shanghai, CN
International Classification:
G06F 3/044
Abstract:
System and methods are provided for touch detection. An example system includes: a measurement unit configured to acquire capacitance measurement data from a touch panel; a pre-processing unit configured to detect whether a touch event occurs on the touch panel based at least in part on the capacitance measurement data and generate an activation signal in response to the detection of the touch event; and a microcontroller unit configured to be activated in response to the activation signal to perform post-processing operations related to the touch event.

Systems And Methods For Tracking Baseline Signals For Touch Detection

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US Patent:
20150242054, Aug 27, 2015
Filed:
Feb 23, 2015
Appl. No.:
14/628606
Inventors:
- St. Michael, BB
Bike Xie - San Jose CA, US
Songping Wu - Cupertino CA, US
International Classification:
G06F 3/041
G06F 3/044
Abstract:
System and methods are provided for tracking baseline signals for touch detection. The system includes: a comparison network configured to determine whether an input baseline signal is within a tracking range; a filter network configured to generate an output baseline signal for touch detection based at least in part on the input baseline signal according to one or more filter parameters; and a signal processing component configured to update the one or more filter parameters based at least in part on the determination of whether the input baseline signal is within the tracking range.
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Bike Xie from San Diego, CA, age ~41 Get Report