公开(公告)号：US20190286885A1

公开(公告)日：2019-09-19

申请号：US15919223

申请日：2018-03-13

申请人： Kneron Inc.

发明人： Chun-Chen Liu

IPC分类号： G06K9/00 ,  G06F21/32 ,  G06N3/04

摘要： A face identification system for a mobile device includes a housing and a central processing unit within the housing, the central processing unit configured to unlock or not unlock the mobile device according to a comparison result. The face identification system is disposed within the housing. The face identification system includes a 3D structured light emitting device configured to emit a three-dimensional structured light signal to an object external to the housing. A first neural network processing unit outputs a comparison result to the central processing unit according to processing of an inputted sampled signal. A sensor is configured to perform three-dimensional sampling of the three-dimensional structured light signal as reflected by the object and input the sampled signal directly to the first neural network processing unit.

公开(公告)号：US20190228210A1

公开(公告)日：2019-07-25

申请号：US15876217

申请日：2018-01-22

申请人： Kneron Inc.

发明人： Chun-Chen Liu

IPC分类号： G06K9/00 ,  G06K9/66

摘要： A face identification system includes a transmitter, a receiver, a database, an artificial intelligence chip, and a main processor. The transmitter is used for emitting at least one first light signal to an object. The receiver is used for receiving at least one second light signal reflected by the object. The database is used for saving training data. The artificial intelligence chip is coupled to the transmitter, the receiver, and the database for identifying a face image from the object according to the at least one second light signal and the training data. The main processor is coupled to the artificial intelligence chip for receiving a face identification signal generated from the artificial intelligence chip.

公开(公告)号：US20170364799A1

公开(公告)日：2017-12-21

申请号：US15182616

申请日：2016-06-15

申请人： Kneron Inc.

发明人： Chun-Chen Liu ,  Kangli Hao ,  Liu Liu

IPC分类号： G06N3/08 ,  G06N3/04

CPC分类号： G06N3/082 ,  G06N3/04

摘要： An apparatus for deciding a simplification policy for a neural network is provided. The deciding apparatus has a plurality of artificial neurons, a receiving circuit, a memory, and a simplifying module. The plurality of artificial neurons are configured to form an original neural network. The receiving circuit receives a set of sample for training the original neural network. The memory is used for recording a plurality of learnable parameters for the original neural network. After the original neural network has been trained with the set of sample, the simplifying module abandons a part of neuron connections in the original neural network based on the learnable parameters recorded by the memory. The simplifying module accordingly decides the structure of a simplified neural network and a plurality of learnable parameters for the simplified neural network.

公开(公告)号：US20240078432A1

公开(公告)日：2024-03-07

申请号：US18508248

申请日：2023-11-14

申请人： Kneron Inc.

发明人： JIE WU ,  JUNJIE SU ,  BIKE XIE ,  Chun-Chen Liu

IPC分类号： G06N3/082 ,  G06N3/04

CPC分类号： G06N3/082 ,  G06N3/04

摘要： A self-tuning model compression methodology for reconfiguring a Deep Neural Network (DNN) includes: receiving a pre-trained DNN model and a data set; performing an inter-layer sparsity analysis to generate a first sparsity result; and performing an intra-layer sparsity analysis to generate a second sparsity result, including: defining a plurality of sparsity metrics for the network; performing forward and backward passes to collect data corresponding to the sparsity metrics; using the collected data to calculate values for the defined sparsity metrics; and visualizing the calculated values using at least a histogram. The methodology further includes: according to the first and second sparsity results, performing low-rank approximation on the pre-trained DNN; pruning the represented DNN model according to the first and second sparsity results; performing quantization on the pruned DNN model according to the first and second sparsity results; and executing the reconfigured model on a user terminal for an end-user application.

公开(公告)号：US10162799B2

公开(公告)日：2018-12-25

申请号：US15459675

申请日：2017-03-15

申请人： Kneron, Inc.

发明人： Yuan Du ,  Li Du ,  Yi-Lei Li ,  Yen-Cheng Kuan ,  Chun-Chen Liu

IPC分类号： G06F17/15 ,  G06F12/02

摘要： A buffer device includes input lines, an input buffer unit and a remapping unit. The input lines are coupled to a memory and configured to be inputted with data from the memory in a current clock. The input buffer unit is coupled to the input lines and configured to buffer one part of the inputted data and output the part of the inputted data in a later clock. The remapping unit is coupled to the input lines and the input buffer unit, and configured to generate remap data for a convolution operation according to the data on the input lines and the output of the input buffer unit in the current clock. A convolution operation method for a data stream is also disclosed.

公开(公告)号：US10943166B2

公开(公告)日：2021-03-09

申请号：US15802092

申请日：2017-11-02

申请人： Kneron, Inc.

发明人： Yuan Du ,  Li Du ,  Chun-Chen Liu

IPC分类号： G06N3/04 ,  G06N3/063

摘要： A pooling operation method for a convolutional neural network includes the following steps of: reading multiple new data in at least one current column of a pooling window; performing a first pooling operation with the new data to generate at least a current column pooling result; storing the current column pooling result in a buffer; and performing a second pooling operation with the current column pooling result and at least a preceding column pooling result stored in the buffer to generate a pooling result of the pooling window. The first pooling operation and the second pooling operation are forward max pooling operations.

公开(公告)号：US10169295B2

公开(公告)日：2019-01-01

申请号：US15459737

申请日：2017-03-15

申请人： Kneron, Inc.

发明人： Li Du ,  Yuan Du ,  Yi-Lei Li ,  Yen-Cheng Kuan ,  Chun-Chen Liu

IPC分类号： G06F17/15 ,  G06F7/544 ,  G06N3/00 ,  G06F7/50 ,  G06T1/20

摘要： A convolution operation method includes the following steps of: performing convolution operations for data inputted in channels, respectively, so as to output a plurality of convolution results; and alternately summing the convolution results of the channels in order so as to output a sum result. A convolution operation device executing the convolution operation method is also disclosed.

公开(公告)号：US10936937B2

公开(公告)日：2021-03-02

申请号：US15801623

申请日：2017-11-02

申请人： Kneron, Inc.

发明人： Li Du ,  Yuan Du ,  Chun-Chen Liu

IPC分类号： G06N3/04 ,  G06N3/063 ,  G06F17/15 ,  G06N3/08

摘要： A convolution operation device includes a convolution calculation module, a memory and a buffer device. The convolution calculation module has a plurality of convolution units, and each convolution unit performs a convolution operation according to a filter and a plurality of current data, and leaves a part of the current data after the convolution operation. The buffer device is coupled to the memory and the convolution calculation module for retrieving a plurality of new data from the memory and inputting the new data to each of the convolution units. The new data are not a duplicate of the current data. A convolution operation method is also disclosed.

公开(公告)号：US20190244011A1

公开(公告)日：2019-08-08

申请号：US15889229

申请日：2018-02-06

申请人： Kneron Inc.

发明人： Chun-Chen Liu

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/00255 ,  G06K9/00201 ,  G06K9/6256

摘要： A low-power face identification method includes emitting at least one first light signal to an object, receiving at least one second light signal reflected by the object, decoding the at least one second light signal to generate a decoded light signal, extracting two-dimensional image information from the decoded light signal, performing a two-dimensional face detection function by an artificial intelligence chip according to the two-dimensional image information and two-dimensional face training data, inhibiting a two-dimensional face recognition function when a two-dimensional face is undetected, and disabling an image converter by the artificial intelligence chip in order to inhibit a three-dimensional face recognition function when the two-dimensional face recognition function is inhibited.

公开(公告)号：US20170330069A1

公开(公告)日：2017-11-16

申请号：US15152528

申请日：2016-05-11

申请人： Kneron Inc.

发明人： Chun-Chen Liu

IPC分类号： G06N3/063 ,  G06N3/04

CPC分类号： G06N3/063 ,  G06N3/04

摘要： A multi-layer artificial neural network including a plurality of artificial neurons, a storage device, and a controller is provided. The plurality of artificial neurons are used for performing computation based on plural parameters. The storage device is used for storing plural sets of parameters, each set of parameters being corresponding to a respective layer. At a first time instant, the controller controls the storage device to provide a set of parameters corresponding to a first layer to the plurality of artificial neurons so that the plurality of artificial neurons form at least part of the first layer. At a second time instant, the controller controls the storage device to provide a set of parameters corresponding to a second layer to the plurality of artificial neurons so that the plurality of artificial neurons format least part of the second layer.