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公开(公告)号：US11157798B2
公开(公告)日：2021-10-26
申请号：US15431606
申请日：2017-02-13
申请人： Brainchip, Inc.
摘要： Embodiments of the present invention provide an artificial neural network system for feature pattern extraction and output labeling. The system comprises a first spiking neural network and a second spiking neural network. The first spiking neural network is configured to autonomously learn complex, temporally overlapping features arising in an input pattern stream. Competitive learning is implemented as spike timing dependent plasticity with lateral inhibition in the first spiking neural network. The second spiking neural network is connected with the first spiking neural network through dynamic synapses, and is trained to interpret and label the output data of the first spiking neural network. Additionally, the labeled output of the second spiking neural network is transmitted to a computing device, such as a central processing unit for post processing.
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公开(公告)号：US11151441B2
公开(公告)日：2021-10-19
申请号：US15428073
申请日：2017-02-08
申请人： Brainchip Inc.
摘要： Embodiments of the present invention provide an artificial neural network system for improved machine learning, feature pattern extraction and output labeling. The system comprises a first spiking neural network and a second spiking neural network. The first spiking neural network is configured to spontaneously learn complex, temporally overlapping features arising in an input pattern stream. Competitive learning is implemented as Spike Timing Dependent Plasticity with lateral inhibition in the first spiking neural network. The second spiking neural network is connected with the first spiking neural network through dynamic synapses, and is trained to interpret and label the output data of the first spiking neural network. Additionally, the output of the second spiking neural network is transmitted to a computing device, such as a CPU for post processing.
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公开(公告)号：US20220147797A1
公开(公告)日：2022-05-12
申请号：US17583640
申请日：2022-01-25
申请人： BrainChip, Inc.
摘要： A system is described that comprises a memory for storing data representative of at least one kernel, a plurality of spiking neuron circuits, and an input module for receiving spikes related to digital data. Each spike is relevant to a spiking neuron circuit and each spike has an associated spatial coordinate corresponding to a location in an input spike array. The system also comprises a transformation module configured to transform a kernel to produce a transformed kernel having an increased resolution relative to the kernel, and/or transform the input spike array to produce a transformed input spike array having an increased resolution relative to the input spike array. The system also comprises a packet collection module configured to collect spikes until a predetermined number of spikes relevant to the input spike array have been collected in a packet in memory, and to organize the collected relevant spikes in the packet based on the spatial coordinates of the spikes, and a convolutional neural processor configured to perform event-based convolution using memory and at least one of the transformed input spike array and the transformed kernel.
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公开(公告)号：US11157800B2
公开(公告)日：2021-10-26
申请号：US15218075
申请日：2016-07-24
申请人： Brainchip Inc.
摘要： A configurable spiking neural network based accelerator system is provided. The accelerator system may be executed on an expansion card which may be a printed circuit board. The system includes one or more application specific integrated circuits comprising at least one spiking neural processing unit and a programmable logic device mounted on the printed circuit board. The spiking neural processing unit includes digital neuron circuits and digital, dynamic synaptic circuits. The programmable logic device is compatible with a local system bus. The spiking neural processing units contain digital circuits comprises a Spiking Neural Network that handles all of the neural processing. The Spiking Neural Network requires no software programming, but can be configured to perform a specific task via the Signal Coupling device and software executing on the host computer. Configuration parameters include the connections between synapses and neurons, neuron types, neurotransmitter types, and neuromodulation sensitivities of specific neurons.
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公开(公告)号：US11704549B2
公开(公告)日：2023-07-18
申请号：US17576103
申请日：2022-01-14
申请人： BrainChip, Inc.
摘要： Embodiments of the present invention provides a system and method of learning and classifying features to identify objects in images using a temporally coded deep spiking neural network, a classifying method by using a reconfigurable spiking neural network device or software comprising configuration logic, a plurality of reconfigurable spiking neurons and a second plurality of synapses. The spiking neural network device or software further comprises a plurality of user-selectable convolution and pooling engines. Each fully connected and convolution engine is capable of learning features, thus producing a plurality of feature map layers corresponding to a plurality of regions respectively, each of the convolution engines being used for obtaining a response of a neuron in the corresponding region. The neurons are modeled as Integrate and Fire neurons with a non-linear time constant, forming individual integrating threshold units with a spike output, eliminating the need for multiplication and addition of floating-point numbers.
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公开(公告)号：US20220138543A1
公开(公告)日：2022-05-05
申请号：US17576103
申请日：2022-01-14
申请人： BrainChip, Inc.
摘要： Embodiments of the present invention provides a system and method of learning and classifying features to identify objects in images using a temporally coded deep spiking neural network, a classifying method by using a reconfigurable spiking neural network device or software comprising configuration logic, a plurality of reconfigurable spiking neurons and a second plurality of synapses. The spiking neural network device or software further comprises a plurality of user-selectable convolution and pooling engines. Each fully connected and convolution engine is capable of learning features, thus producing a plurality of feature map layers corresponding to a plurality of regions respectively, each of the convolution engines being used for obtaining a response of a neuron in the corresponding region. The neurons are modeled as Integrate and Fire neurons with a non-linear time constant, forming individual integrating threshold units with a spike output, eliminating the need for multiplication and addition of floating-point numbers.
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公开(公告)号：US11227210B2
公开(公告)日：2022-01-18
申请号：US16938254
申请日：2020-07-24
申请人： BrainChip, Inc.
摘要： Embodiments of the present invention provides a system and method of learning and classifying features to identify objects in images using a temporally coded deep spiking neural network, a classifying method by using a reconfigurable spiking neural network device or software comprising configuration logic, a plurality of reconfigurable spiking neurons and a second plurality of synapses. The spiking neural network device or software further comprises a plurality of user-selectable convolution and pooling engines. Each fully connected and convolution engine is capable of learning features, thus producing a plurality of feature map layers corresponding to a plurality of regions respectively, each of the convolution engines being used for obtaining a response of a neuron in the corresponding region. The neurons are modeled as Integrate and Fire neurons with a non-linear time constant, forming individual integrating threshold units with a spike output, eliminating the need for multiplication and addition of floating-point numbers.
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公开(公告)号：US20190188600A1
公开(公告)日：2019-06-20
申请号：US16282550
申请日：2019-02-22
申请人： BrainChip, Inc.
摘要： Disclosed herein are system and method embodiments for establishing secure communication with a remote artificial intelligent device. An embodiment operates by capturing an auditory signal from an auditory source. The embodiment coverts the auditory signal into a plurality of pulses having a spatio-temporal distribution. The embodiment identifies an acoustic signature in the auditory signal based on the plurality of pulses using a spatio-temporal neural network. The embodiment modifies synaptic strengths in the spatio-temporal neural network in response to the identifying thereby causing the spatio-temporal neural network to learn to respond to the acoustic signature in the acoustic signal. The embodiment transmits the plurality of pulses to the remote artificial intelligent device over a communications channel thereby causing the remote artificial intelligent device to learn to respond to the acoustic signature, and thereby allowing secure communication to be established with the remote artificial intelligent device based on the auditory signature.
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公开(公告)号：US11853862B2
公开(公告)日：2023-12-26
申请号：US16349248
申请日：2017-11-20
申请人： BrainChip, Inc.
发明人： Simon Thorpe , Timothée Masquelier , Jacob Martin , Amir Reza Yousefzadeh , Bernabe Linares-Barranco
CPC分类号： G06N3/049 , G06F18/2148 , G06F18/2178 , G06F18/22 , G06N3/063 , G06N3/088 , G06V20/41 , G06V20/44
摘要： A method of performing unsupervised detection of repeating patterns in a series (TS) of events (E21, E12, E5, . . . ), comprising the steps of: a) Providing a plurality of neurons (NR1-NRP), each neuron being representative of W event types; b) Acquiring an input packet (IV) comprising N successive events of the series; c) Attributing to at least some neurons a potential value (PT1-PTP), representative of the number of common events between the input packet and the neuron; d) Modifying the event types of neurons having a potential value exceeding a first threshold TL; and e) Generating a first output signal (OS1-OSP) for all neurons having a potential value exceeding a second threshold TF, and a second output signal, different from the first one, for all other neurons. A digital electronic circuit and system configured for carrying out the above method.
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公开(公告)号：US20230026363A1
公开(公告)日：2023-01-26
申请号：US17962082
申请日：2022-10-07
申请人： BrainChip, Inc.
摘要： Disclosed herein are system, method, and computer program product embodiments for an improved spiking neural network (SNN) configured to learn and perform unsupervised extraction of features from an input stream. An embodiment operates by receiving a set of spike bits corresponding to a set synapses associated with a spiking neuron circuit. The embodiment applies a first logical AND function to a first spike bit in the set of spike bits and a first synaptic weight of a first synapse in the set of synapses. The embodiment increments a membrane potential value associated with the spiking neuron circuit based on the applying. The embodiment determines that the membrane potential value associated with the spiking neuron circuit reached a learning threshold value. The embodiment then performs a Spike Time Dependent Plasticity (STDP) learning function based on the determination that the membrane potential value of the spiking neuron circuit reached the learning threshold value.
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