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公开(公告)号：US20220092407A1
公开(公告)日：2022-03-24
申请号：US17029506
申请日：2020-09-23
发明人： Pin-Yu Chen , Sijia Liu , Chia-Yu Chen , I-Hsin Chung , Tsung-Yi Ho , Yun-Yun Tsai
摘要： Transfer learning in machine learning can include receiving a machine learning model. Target domain training data for reprogramming the machine learning model using transfer learning can be received. The target domain training data can be transformed by performing a transformation function on the target domain training data. Output labels of the machine learning model can be mapped to target labels associated with the target domain training data. The transformation function can be trained by optimizing a parameter of the transformation function. The machine learning model can be reprogrammed based on input data transformed by the transformation function and a mapping of the output labels to target labels.
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公开(公告)号：US12061991B2
公开(公告)日：2024-08-13
申请号：US17029506
申请日：2020-09-23
发明人： Pin-Yu Chen , Sijia Liu , Chia-Yu Chen , I-Hsin Chung , Tsung-Yi Ho , Yun-Yun Tsai
IPC分类号： G06N3/094 , G06N3/08 , G06N3/096 , G06N20/00 , G06F18/213 , G06F18/2134 , G06F18/214
CPC分类号： G06N3/094 , G06N3/08 , G06N3/096 , G06N20/00 , G06F18/213 , G06F18/21347 , G06F18/214
摘要： Transfer learning in machine learning can include receiving a machine learning model. Target domain training data for reprogramming the machine learning model using transfer learning can be received. The target domain training data can be transformed by performing a transformation function on the target domain training data. Output labels of the machine learning model can be mapped to target labels associated with the target domain training data. The transformation function can be trained by optimizing a parameter of the transformation function. The machine learning model can be reprogrammed based on input data transformed by the transformation function and a mapping of the output labels to target labels.
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公开(公告)号：US20240045974A1
公开(公告)日：2024-02-08
申请号：US18382107
申请日：2023-10-20
发明人： Pin-Yu Chen , Sijia Liu , Lingfei Wu , Chia-Yu Chen
IPC分类号： G06F21/57 , G06N3/04 , G06N3/08 , G06V10/764 , G06V10/82
CPC分类号： G06F21/577 , G06N3/04 , G06N3/08 , G06V10/764 , G06V10/82 , G06F2221/034
摘要： An adversarial robustness testing method, system, and computer program product include testing, via an accelerator, a robustness of a black-box system under different access settings, where the testing includes tearing down the robustness testing to a subtask of a predetermined size.
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公开(公告)号：US20220180171A1
公开(公告)日：2022-06-09
申请号：US17112528
申请日：2020-12-04
发明人： Xiao Sun , Ankur Agrawal , Kailash Gopalakrishnan , Naigang Wang , Chia-Yu Chen , Jiamin Ni
摘要： An apparatus includes a floating-point gradient register; an integer register; a memory bank; and an array of processing units. Each of the units includes a plurality of binary shifters having an integer input configured to obtain corresponding bits of a 4-bit integer multiplicand, and a shift-specifying input configured to obtain corresponding bits in an exponent field of a 4-bit floating point multiplier. The multiplier is specified in a mantissaless four-bit floating point format including a sign bit, three exponent bits, and no mantissa bits. An adder tree has a plurality of inputs coupled to outputs of the plurality of shifters, and a rounder has an input coupled to an output of the adder tree. The integer inputs are connected to the integer register; the shift-specifying inputs are connected to the floating-point gradient register; and outputs of the rounders are coupled to the memory bank.
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公开(公告)号：US11295208B2
公开(公告)日：2022-04-05
申请号：US15830170
申请日：2017-12-04
摘要： Embodiments of the present invention provide a computer-implemented method for adaptive residual gradient compression for training of a deep learning neural network (DNN). The method includes obtaining, by a first learner, a current gradient vector for a neural network layer of the DNN, in which the current gradient vector includes gradient weights of parameters of the neural network layer that are calculated from a mini-batch of training data. A current residue vector is generated that includes residual gradient weights for the mini-batch. A compressed current residue vector is generated based on dividing the residual gradient weights of the current residue vector into a plurality of bins of a uniform size and quantizing a subset of the residual gradient weights of one or more bins of the plurality of bins. The compressed current residue vector is then transmitted to a second learner of the plurality of learners or to a parameter server.
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公开(公告)号：US20210117771A1
公开(公告)日：2021-04-22
申请号：US16657263
申请日：2019-10-18
发明人： Chia-Yu Chen , Pin-Yu Chen , Mingu Kang , Jintao Zhang
摘要： Methods, systems, and circuits for training a neural network include applying noise to a set of training data across wordlines using a respective noise switch on each wordline. A neural network is trained using the noise-applied training data to generate a classifier that is robust against adversarial training.
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公开(公告)号：US20210064976A1
公开(公告)日：2021-03-04
申请号：US16558554
申请日：2019-09-03
发明人： Xiao Sun , Jungwook Choi , Naigang Wang , Chia-Yu Chen , Kailash Gopalakrishnan
摘要： An apparatus includes circuitry for a neural network that is configured to perform forward propagation neural network operations on floating point numbers having a first n-bit floating point format. The first n-bit floating point format has a configuration consisting of a sign bit, m exponent bits and p mantissa bits where m is greater than p. The circuitry is further configured to perform backward propagation neural network operations on floating point numbers having a second n-bit floating point format that is different than the first n-bit floating point format. The second n-bit floating point format has a configuration consisting of a sign bit, q exponent bits and r mantissa bits where q is greater than m and r is less than p.
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公开(公告)号：US10529717B2
公开(公告)日：2020-01-07
申请号：US14865667
申请日：2015-09-25
发明人： Chia-Yu Chen , Bruce B. Doris , Hong He , Rajasekhar Venigalla
IPC分类号： H01L29/04 , H01L29/66 , H01L29/78 , H01L27/092 , H01L21/18 , H01L29/165 , H01L21/8238 , H01L21/84 , H01L27/12
摘要： A semiconductor device that includes at least one germanium containing fin structure having a length along a direction and a sidewall orientated along the (100) plane. The semiconductor device also includes at least one germanium free fin structure having a length along a direction and a sidewall orientated along the (100) plane. A gate structure is present on a channel region of each of the germanium containing fin structure and the germanium free fin structure. N-type epitaxial semiconductor material having a square geometry present on the source and drain portions of the sidewalls having the (100) plane orientation of the germanium free fin structures. P-type epitaxial semiconductor material having a square geometry is present on the source and drain portions of the sidewalls having the (100) plane orientation of the germanium containing fin structures.
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公开(公告)号：US20190171935A1
公开(公告)日：2019-06-06
申请号：US15830170
申请日：2017-12-04
摘要： Embodiments of the present invention provide a computer-implemented method for adaptive residual gradient compression for training of a deep learning neural network (DNN). The method includes obtaining, by a first learner, a current gradient vector for a neural network layer of the DNN, in which the current gradient vector includes gradient weights of parameters of the neural network layer that are calculated from a mini-batch of training data. A current residue vector is generated that includes residual gradient weights for the mini-batch. A compressed current residue vector is generated based on dividing the residual gradient weights of the current residue vector into a plurality of bins of a uniform size and quantizing a subset of the residual gradient weights of one or more bins of the plurality of bins. The compressed current residue vector is then transmitted to a second learner of the plurality of learners or to a parameter server.
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公开(公告)号：US10229982B2
公开(公告)日：2019-03-12
申请号：US15629910
申请日：2017-06-22
发明人： Chia-Yu Chen , Zuoguang Liu , Sanjay C. Mehta , Tenko Yamashita
IPC分类号： H01L29/45 , H01L29/08 , H01L29/16 , H01L29/40 , H01L29/41 , H01L29/49 , H01L29/51 , H01L29/66 , H01L29/78 , H01L23/48 , H01L21/768 , H01L21/225 , H01L29/417 , H01L21/8238 , H01L21/3065 , H01L21/311 , H01L29/167 , H01L21/285 , H01L23/485
摘要： A semiconductor device includes a gate disposed over a substrate; a source region and a drain region on opposing sides of the gate; and a pair of trench contacts over and abutting an interfacial layer portion of at least one of the source region and the drain region; wherein the interfacial layer includes boron in an amount in a range from about 5×1021 to about 5×1022 atoms/cm2.
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