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1. 发明授权

US10970622B2 Dynamic gating using neuromorphic hardware 有权
公开(公告)号：US10970622B2
公开(公告)日：2021-04-06
申请号：US15405824
申请日：2017-01-13
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos
IPC分类号： G06N3/063 , G06N3/04
摘要： Dynamic gating for neuromorphic systems and the configuration thereof are provided. In various embodiments, neurosynaptic system comprises a neurosynaptic core. The neuromorphic core comprises a plurality of neurons and axons. The neurosynaptic core comprises a programmable gate operative to receive a control signal and selectively output a first output signal based on the control signal. In various embodiments, a plurality of input parameters are read, defining the behavior of a programmable gate. Based upon the plurality of input parameters, a neurosynaptic core is configured to provide a programmable gate operative to receive a control signal and selectively output a first output signal based on the control signal.

2. 发明申请

US20200242457A1 HIGH DYNAMIC RANGE, HIGH CLASS COUNT, HIGH INPUT RATE WINNER-TAKE-ALL ON NEUROMORPHIC HARDWARE 审中-公开
公开(公告)号：US20200242457A1
公开(公告)日：2020-07-30
申请号：US16842512
申请日：2020-04-07
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos , Steven K. Esser , Jeffrey A. Kusnitz
IPC分类号： G06N3/063 , G06N3/04
摘要： High dynamic range, high class count, high input rate winner-take-all on neuromorphic hardware is provided. In some embodiments, a plurality of thermometer codes are received by a neurosynaptic core. The plurality of thermometer codes are split into a plurality of intervals. One of the plurality of intervals is selected. A local maximum is determined within the one of the plurality of intervals. A global maximum is determined based on the local maximum.

3. 发明申请

US20190042886A1 SCENE UNDERSTANDING USING A NEUROSYNAPTIC SYSTEM 审中-公开
公开(公告)号：US20190042886A1
公开(公告)日：2019-02-07
申请号：US16147106
申请日：2018-09-28
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos , Rathinakumar Appuswamy , Pallab Datta , Steven K. Esser , Dharmendra S. Modha
IPC分类号： G06K9/62 , H04N9/67 , G06K9/46 , G06T7/246 , G06N3/08 , G06N3/063 , G06K9/66 , G06K9/52 , H04N19/136 , G06K9/00
摘要： Embodiments of the invention provide a method for scene understanding based on a sequence of image frames. The method comprises converting each pixel of each image frame to neural spikes, and extracting features from the sequence of image frames by processing neural spikes corresponding to pixels of the sequence of image frames. The method further comprises encoding the extracted features as neural spikes, and classifying the extracted features.

4. 发明授权

US10043110B2 Scene understanding using a neurosynaptic system 有权
公开(公告)号：US10043110B2
公开(公告)日：2018-08-07
申请号：US15298137
申请日：2016-10-19
申请人： International Business Machines Corporation
发明人： Alexander Andreopoulos , Rathinakumar Appuswamy , Pallab Datta , Steven K. Esser , Dharmendra S. Modha
IPC分类号： G06K9/62 , G06K9/46 , H04N19/136 , G06K9/52 , G06K9/66 , G06N3/063 , G06N3/08 , G06T7/246 , G06K9/00 , H04N9/67
摘要： Embodiments of the invention provide a method for scene understanding based on a sequence of image frames. The method comprises converting each pixel of each image frame to neural spikes, and extracting features from the sequence of image frames by processing neural spikes corresponding to pixels of the sequence of image frames. The method further comprises encoding the extracted features as neural spikes, and classifying the extracted features.

5. 发明申请

US20180107893A1 EXTRACTING MOTION SALIENCY FEATURES FROM VIDEO USING A NEUROSYNAPTIC SYSTEM 审中-公开
公开(公告)号：US20180107893A1
公开(公告)日：2018-04-19
申请号：US15855820
申请日：2017-12-27
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos , Steven K. Esser , Dharmendra S. Modha
IPC分类号： G06K9/46 , G06K9/66 , G06K9/00
摘要： Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input video of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes.

6. 发明申请

US20170287119A1 LENS DISTORTION CORRECTION USING A NEUROSYNAPTIC SYSTEM 审中-公开
公开(公告)号：US20170287119A1
公开(公告)日：2017-10-05
申请号：US15495472
申请日：2017-04-24
申请人： International Business Machines Corporation
发明人： Alexander Andreopoulos , Daniel F. Gruhl , Michael Karasick , Dharmendra S. Modha
IPC分类号： G06T5/00 , G06T1/20 , G06N3/063
CPC分类号： G06T3/4046 , G06F17/11 , G06K9/4604 , G06K9/66 , G06N3/063 , G06T1/20 , G06T5/006 , G06T2207/20084 , G06T2207/20172
摘要： One or more embodiments provide a system and circuit for image distortion correction. The system includes neurosynaptic core circuits that: receive a set of inputs comprising image dimensions and pixel distortion coefficients for at least one image frame via at least one input core circuit, map each distorted pixel to zero or more undistorted pixels by processing the set of inputs corresponding to each pixel of the at least one image frame by the at least one input core circuit, and route corresponding pixel intensity values of each distorted pixel to output undistorted pixels for each image frame via at least one output core circuit.

7. 发明授权

US09195903B2 Extracting salient features from video using a neurosynaptic system 有权
标题翻译：使用神经突触系统从视频中提取突出特征
公开(公告)号：US09195903B2
公开(公告)日：2015-11-24
申请号：US14265268
申请日：2014-04-29
申请人： International Business Machines Corporation
发明人： Alexander Andreopoulos , Steven K. Esser , Dharmendra S. Modha
IPC分类号： G06K9/00 , G06K9/46 , G06K9/66
CPC分类号： G06K9/4671 , G06K9/00744 , G06K9/4604 , G06K9/4652 , G06K9/4676 , G06K9/66 , G06N3/049 , G06N3/063
摘要： Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input sequence of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes.
摘要翻译：本发明的实施例提供一种视觉显着性估计方法，包括接收图像帧的输入序列。每个图像帧具有一个或多个通道，并且每个通道具有一个或多个像素。该方法还包括对于每个图像帧的每个通道，基于通道的每个像素的像素强度生成相应的神经峰值数据，基于相应的神经峰值数据生成相应的多尺度数据结构，并且提取相应的从相应的多尺度数据结构的特征图。多尺度数据结构包括一个或多个数据层，其中每个数据层表示相应尺度的通道的像素强度的尖峰表示。该方法还包括对作为神经尖峰提取的特征的每个图进行编码。

8. 发明授权

US11361214B2 Dynamic multiscale routing on networks of neurosynaptic cores 有权
公开(公告)号：US11361214B2
公开(公告)日：2022-06-14
申请号：US15406211
申请日：2017-01-13
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos
IPC分类号： G06N3/04 , G06N3/063
摘要： Dynamic multiscale routing on networks of neurosynaptic cores with a feedback attention beam and short term memory with inhibition of return is provided. In various embodiments, an input topographic map is received at a spiking neuromorphic hardware system. A saliency map is received, associating a saliency value with each of a plurality of regions of the input topographic map. Based on the saliency map, a first of the plurality of regions in order of saliency value is routed. The first of the plurality of regions is suppressed. Based on the saliency map, a predetermined number of the plurality of regions are sequentially routed in order of saliency value.

9. 发明授权

US11120561B2 Detection, tracking and recognition on networks of digital neurosynaptic cores 有权
公开(公告)号：US11120561B2
公开(公告)日：2021-09-14
申请号：US16177011
申请日：2018-10-31
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos , Arnon Amir , Tapan K. Nayak
IPC分类号： G06T7/246 , G06N3/04 , G06K9/62
摘要： Detection, tracking and recognition on networks of digital neurosynaptic cores are provided. In various embodiments, an image sensor is configured to provide a time-series of frames. A first artificial neural network is operatively coupled to the image sensor and configured to detect a plurality of objects in the time-series of frames. A second artificial neural network is operatively coupled to the first artificial neural network and configured to classify objects detected by the first neural network and output a location and classification of said classified objects. The first and second artificial neural networks comprise one or more spike-based neurosynaptic cores.

10. 发明授权

US10528843B2 Extracting motion saliency features from video using a neurosynaptic system 有权
公开(公告)号：US10528843B2
公开(公告)日：2020-01-07
申请号：US15855820
申请日：2017-12-27
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： Alexander Andreopoulos , Steven K. Esser , Dharmendra S. Modha
IPC分类号： G06K9/00 , G06K9/46 , G06K9/66 , G06N3/04 , G06N3/063
摘要： Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input video of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes.

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