Wireless devices and systems including examples of mixing input data with coefficient data

    公开(公告)号:US11658687B2

    公开(公告)日:2023-05-23

    申请号:US17394597

    申请日:2021-08-05

    CPC classification number: H04B1/0039 H04L27/2601

    Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data. For example, a computing system with processing units may mix the input data for a transmission in a radio frequency (RF) wireless domain with the coefficient data to generate output data that is representative of the transmission being processed according to the wireless protocol in the RF wireless domain. A computing device may be trained to generate coefficient data based on the operations of a wireless transceiver such that mixing input data using the coefficient data generates an approximation of the output data, as if it were processed by the wireless transceiver. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.

    Memory including examples of calculating hamming distances for neural network and data center applications

    公开(公告)号:US11636285B2

    公开(公告)日:2023-04-25

    申请号:US17016074

    申请日:2020-09-09

    Abstract: Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory die. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing command, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed on a memory die itself, like a memory die of a NAND memory device.

    Memory controllers including examples of calculating hamming distances for neural network and data center applications

    公开(公告)号:US11609853B2

    公开(公告)日:2023-03-21

    申请号:US17016053

    申请日:2020-09-09

    Abstract: Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory controller with various memory devices. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing request, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed “closer” to the memory devices, e.g., at the memory controller coupled to memory devices.

    Memory systems including examples of calculating hamming distances for neural network and data center applications

    公开(公告)号:US11586380B2

    公开(公告)日:2023-02-21

    申请号:US17016023

    申请日:2020-09-09

    Abstract: Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory system including a Hamming processing unit. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing request, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed “closer” to the memory devices, e.g., at a processing unit having memory devices.

    Memory devices and methods which may facilitate tensor memory access with memory maps based on memory operations

    公开(公告)号:US11573903B2

    公开(公告)日:2023-02-07

    申请号:US16864437

    申请日:2020-05-01

    Abstract: Examples described herein include systems and methods which include an apparatus comprising a memory array including a plurality of memory cells and a memory controller coupled to the memory array. The memory controller comprises a memory mapper configured to configure a memory map on the basis of a memory command associated with a memory access operation. The memory map comprises a specific sequence of memory access instructions to access at least one memory cell of the memory array. For example, the specific sequence of memory access instructions for a diagonal memory command comprises a sequence of memory access instructions that each access a memory cell along a diagonal of the memory array.

    Wireless devices and systems including examples of cross correlating wireless transmissions

    公开(公告)号:US11539502B2

    公开(公告)日:2022-12-27

    申请号:US17090123

    申请日:2020-11-05

    Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of cross correlation including symbols indicative of radio frequency (RF) energy. An electronic device including a statistic calculator may be configured to calculate a statistic including the cross-correlation of the symbols. The electronic device may include a comparator configured to provide a signal indicative of a presence or absence of a wireless communication signal in the particular portion of the wireless spectrum based on a comparison of the statistic with a threshold. A decoder/precoder may be configured to receive the signal indicative of the presence or absence of the wireless communication signal and to decode the symbols responsive to a signal indicative of the presence of the wireless communication signal. Examples of systems and methods described herein may facilitate the processing of data for wireless communications in a power-efficient and time-efficient manner.

    Memory devices and methods which may facilitate tensor memory access

    公开(公告)号:US11422929B2

    公开(公告)日:2022-08-23

    申请号:US17150675

    申请日:2021-01-15

    Abstract: Methods, apparatuses, and systems for tensor memory access are described. Multiple data located in different physical addresses of memory may be concurrently read or written by, for example, employing various processing patterns of tensor or matrix related computations. A memory controller, which may comprise a data address generator, may be configured to generate a sequence of memory addresses for a memory access operation based on a starting address and a dimension of a tensor or matrix. At least one dimension of a tensor or matrix may correspond to a row, a column, a diagonal, a determinant, or an Nth dimension of the tensor or matrix. The memory controller may also comprise a buffer configured to read and write the data generated from or according to a sequence of memory of addresses.

    Memory systems including examples of calculating hamming distances for neural network and data center applications

    公开(公告)号:US11327682B2

    公开(公告)日:2022-05-10

    申请号:US17016023

    申请日:2020-09-09

    Abstract: Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory system including a Hamming processing unit. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing request, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed “closer” to the memory devices, e.g., at a processing unit having memory devices.

    Neuron calculator for artificial neural networks

    公开(公告)号:US11070257B2

    公开(公告)日:2021-07-20

    申请号:US16786637

    申请日:2020-02-10

    Abstract: Examples described herein include systems and methods, including wireless devices and systems with neuron calculators that may perform one or more functionalities of a wireless transceiver. The neuron calculator calculates output signals that may be implemented, for example, using accumulation units that sum the multiplicative processing results of ordered sets from ordered neurons with connection weights for each connection between an ordered neuron and outputs of the neuron calculator. The ordered sets may be a combination of some input signals, with the number of signals determined by an order of the neuron. Accordingly, a kth-order neuron may include an ordered set comprising product values of k input signals, where the input signals are selected from a set of k-combinations with repetition. As an example in a wireless transceiver, the neuron calculator may perform channel estimation as a channel estimation processing component of the receiver portion of a wireless transceiver.

    Wireless devices and systems including examples of configuration during an active time period

    公开(公告)号:US11032139B2

    公开(公告)日:2021-06-08

    申请号:US16116849

    申请日:2018-08-29

    Abstract: Examples described herein include methods, devices, and systems which may implement different processing stages for wireless communication in processing units. Such data processing may include a source data processing stage, a baseband processing stage, a digital front-end processing stage, and a radio frequency (RF) processing stage. Data may be received from a sensor of device and then processed in the stages to generate output data for transmission. Processing the data in the various stages may occur during an active time period of a discontinuous operating mode. During the active time period, a reconfigurable hardware platform may allocate all or a portion of the processing units to implement the processing stages. Examples of systems and methods described herein may facilitate the processing of data for 5G (e.g., New Radio (NR)) wireless communications in a power-efficient and time-efficient manner.

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