Samsung Electronics Co., Ltd.

Zhengping Ji

Ilia Ovsiannikov

Yibing Michelle Wang

Lilong Shi

Qiang Zhang

John Wakefield Brothers

Hyun Surk Ryu

Weiran Deng

Kyoobin Lee

Eunsoo Shim

Hongyu Wang

An embodiment includes a method, comprising: pruning a layer of a neural network having multiple layers using a threshold; and repeating the pruning of the layer of the neural network using a different threshold until a pruning error of the pruned layer reaches a pruning error allowance.

Automatic thresholds for neural network pruning and retraining

A method of manufacturing an apparatus is provided. The apparatus is formed on a wafer or a package. The apparatus includes a polynomial generator, a plurality of matrix generators connected to an output of the polynomial generator, and a convolution generator connected to an output of the plurality of matrix generators. The apparatus is tested using one or more electrical to optical converters, one or more optical splitters, and one or more optical to electrical converters.

Apparatus and method for generating efficient convolution

A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Object recognition with reduced neural network weight precision

A system and method to reduce weight storage bits for a deep-learning network includes a quantizing module and a cluster-number reduction module. The quantizing module quantizes neural weights of each quantization layer of the deep-learning network. The cluster-number reduction module reduces the predetermined number of clusters for a layer having a clustering error that is a minimum of the clustering errors of the plurality of quantization layers. The quantizing module requantizes the layer based on the reduced predetermined number of clusters for the layer and the cluster-number reduction module further determines another layer having a clustering error that is a minimum of the clustering errors of the plurality of quantized layers and reduces the predetermined number of clusters for the another layer until a recognition performance of the deep-learning network has been reduced by a predetermined threshold.

Method and algorithm of recursive deep learning quantization for weight bit reduction

A method of manufacturing an apparatus and a method of constructing an integrated circuit are provided. The method of manufacturing an apparatus includes forming the apparatus on a wafer or a package with at least one other apparatus, wherein the apparatus comprises a polynomial generator, a first matrix generator, a second matrix generator, a third matrix generator, and a convolution generator; and testing the apparatus, wherein testing the apparatus comprises testing the apparatus using one or more electrical to optical converters, one or more optical splitters that split an optical signal into two or more optical signals, and one or more optical to electrical converters.

A Dynamic Vision Sensor (DVS) pose-estimation system includes a DVS, a transformation estimator, an inertial measurement unit (IMU) and a camera-pose estimator based on sensor fusion. The DVS detects DVS events and shapes frames based on a number of accumulated DVS events. The transformation estimator estimates a 3D transformation of the DVS camera based on an estimated depth and matches confidence-level values within a camera-projection model such that at least one of a plurality of DVS events detected during a first frame corresponds to a DVS event detected during a second subsequent frame. The IMU detects inertial movements of the DVS with respect to world coordinates between the first and second frames. The camera-pose estimator combines information from a change in a pose of the camera-projection model between the first frame and the second frame based on the estimated transformation and the detected inertial movements of the DVS.

Multiscale weighted matching and sensor fusion for dynamic vision sensor tracking

An image signal processing (ISP) system is provided. The system includes a neural network trained by inputting a set of raw data images and a correlating set of desired quality output images; the neural network including an input for receiving input image data and providing processed output; wherein the processed output includes input image data that has been adjusted for at least one image quality attribute. A method and an imaging device are disclosed.

Method and apparatus of neural network based image signal processor

A method for configuring a neural network is provided. The method includes: selecting a neural network including a plurality of layers, each of the layers including a plurality of neurons for processing an input and providing an output; and, incorporating at least one switch configured to randomly select and disable at least a portion of the neurons in each layer. Another method in the computer program product is disclosed.

Method and apparatus of learning neural network via hierarchical ensemble learning

An apparatus and a method is provided. The apparatus includes a polynomial generator, including an input and an output; a first matrix generator, including an input connected to the output of the polynomial generator, and an output; a second matrix generator, including an input connected to the output of the first matrix generator, and an output; a third matrix generator, including a first input connected to the output of the first matrix generator, a second input connected to the output of the second matrix generator, and an output; and a convolution generator, including an input connected to the output of the third matrix generator, and an output.

An apparatus and a method. The apparatus includes a dynamic vision sensor (DVS) configured to generate a stream of events, where an event includes a location and a binary value indicating a positive or a negative change in luminance; a sampling unit connected to the DVS and configured to sample the stream of events; and an image formation unit connected to the sampling unit and configured to form an image for each sample of the stream of events, wherein a manner of sampling by the sampling unit is adjusted to reduce variations between images formed by the image formation unit.

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Temple City, CA, United States of America

Zhengping Ji