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公开(公告)号：US20220351029A1
公开(公告)日：2022-11-03
申请号：US17811412
申请日：2022-07-08
申请人： Recogni Inc.
发明人： Eugene M. Feinberg
摘要： A convolutional engine is configured to process input data that is organized into horizontal stripes. The number of accumulators present in each convolver unit of the convolutional engine may equal a total number of rows of data in each of the horizontal stripes.
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公开(公告)号：US11694069B2
公开(公告)日：2023-07-04
申请号：US17811417
申请日：2022-07-08
申请人： Recogni Inc.
发明人： Eugene M. Feinberg
摘要： Contiguous columns of a convolutional engine are partitioned into two or more groups. Each group of columns may be used to process input data. Filter weights assigned to one group may be distinct from filter weights assigned to another group.
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公开(公告)号：US10922585B2
公开(公告)日：2021-02-16
申请号：US16273604
申请日：2019-02-12
申请人： Recogni Inc.
摘要： Labeled data is deterministically generated for training or validating machine learning models for image analysis. Approaches are described that allow this training data to be generated, for example, in real-time, and in response to the conditions at the location where images are generated by image sensors.
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公开(公告)号：US20190287297A1
公开(公告)日：2019-09-19
申请号：US16273618
申请日：2019-02-12
申请人： Recogni Inc.
发明人： Shabarivas Abhiram , Gilles J. C. A. Backhus , Eugene M. Feinberg , Berend Ozceri , Martin Stefan Patz
摘要： Systems, methods, and machine-readable media for determining a three-dimensional environment model of the environment of one or more camera devices, in which image processing for inferring the model may be performed at the camera devices, are described.
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公开(公告)号：US20220351030A1
公开(公告)日：2022-11-03
申请号：US17811416
申请日：2022-07-08
申请人： Recogni Inc.
发明人： Eugene M. Feinberg
摘要： A convolutional engine is configured to process input data that is organized into vertical stripes.
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公开(公告)号：US20220343169A1
公开(公告)日：2022-10-27
申请号：US17811186
申请日：2022-07-07
申请人： Recogni Inc.
摘要： A method for instantiating a convolutional neural network on a computing system. The convolutional neural network includes a plurality of layers, and instantiating the convolutional neural network includes training the convolutional neural network using a first loss function until a first classification accuracy is reached, clustering a set of F×K kernels of the first layer into a set of C clusters, training the convolutional neural network using a second loss function until a second classification accuracy is reached, creating a dictionary which maps each of a number of centroids to a corresponding centroid identifier, quantizing and compressing F filters of the first layer, storing F quantized and compressed filters of the first layer in a memory of the computing system, storing F biases of the first layer in the memory, and classifying data received by the convolutional neural network.
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公开(公告)号：US11468316B2
公开(公告)日：2022-10-11
申请号：US16273592
申请日：2019-02-12
申请人： Recogni Inc.
摘要： A method for instantiating a convolutional neural network on a computing system. The convolutional neural network includes a plurality of layers, and instantiating the convolutional neural network includes training the convolutional neural network using a first loss function until a first classification accuracy is reached, clustering a set of F×K kernels of the first layer into a set of C clusters, training the convolutional neural network using a second loss function until a second classification accuracy is reached, creating a dictionary which maps each of a number of centroids to a corresponding centroid identifier, quantizing and compressing F filters of the first layer, storing F quantized and compressed filters of the first layer in a memory of the computing system, storing F biases of the first layer in the memory, and classifying data received by the convolutional neural network.
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公开(公告)号：US20220076104A1
公开(公告)日：2022-03-10
申请号：US16948164
申请日：2020-09-04
申请人： Recogni Inc.
发明人： Jian hui Huang , James Michael Bodwin , Pradeep R. Joginipally , Shabarivas Abhiram , Gary S. Goldman , Martin Stefan Patz , Eugene M. Feinberg , Berend Ozceri
摘要： Dynamic data quantization may be applied to minimize the power consumption of a system that implements a convolutional neural network (CNN). Under such a quantization scheme, a quantized representation of a 3×3 array of m-bit activation values may include 9 n-bit mantissa values and one exponent shared between the n-bit mantissa values (n
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公开(公告)号：US20190286980A1
公开(公告)日：2019-09-19
申请号：US16273592
申请日：2019-02-12
申请人： Recogni Inc.
摘要： A method for instantiating a convolutional neural network on a computing system. The convolutional neural network includes a plurality of layers, and instantiating the convolutional neural network includes training the convolutional neural network using a first loss function until a first classification accuracy is reached, clustering a set of F×K kernels of the first layer into a set of C clusters, training the convolutional neural network using a second loss function until a second classification accuracy is reached, creating a dictionary which maps each of a number of centroids to a corresponding centroid identifier, quantizing and compressing F filters of the first layer, storing F quantized and compressed filters of the first layer in a memory of the computing system, storing F biases of the first layer in the memory, and classifying data received by the convolutional neural network.
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公开(公告)号：US20190286975A1
公开(公告)日：2019-09-19
申请号：US16273616
申请日：2019-02-12
申请人： Recogni Inc.
发明人： Eugene M. Feinberg
摘要： A hardware architecture for implementing a convolutional neural network.
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