Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of an autocorrelation calculator. An electronic device including an autocorrelation calculator may be configured to calculate an autocorrelation matrix including an autocorrelation of symbols indicative of a first narrowband Internet of Things (IoT) transmission and a second narrowband IoT transmission. The electronic device may calculate the autocorrelation matrix based on a stored autocorrelation matrix and the autocorrelation of symbols indicative of the first narrowband IoT transmission and symbols indicative of the second narrowband IoT transmission. The stored autocorrelation matrix may represent another received signal at a different time period than a time period of the first and second narrowband IoT transmission.

Abstract: Systems, apparatuses, and methods for organizing bits in a memory device are described. In a number of embodiments, an apparatus can include an array of memory cells, a data interface, a multiplexer coupled between the array of memory cells and the data interface, and a controller coupled to the array of memory cells, the controller configured to cause the apparatus to latch bits associated with a row of memory cells in the array in a number of sense amplifiers in a prefetch operation and send the bits from the sense amplifiers, through a multiplexer, to a data interface, which may include or be referred to as DQs. The bits may be sent to the DQs in a particular order that may correspond to a particular matrix configuration and may thus facilitate or reduce the complexity of arithmetic operations performed on the data.

Abstract: Methods and apparatus for incentivizing device participation within a distributed network. In one exemplary embodiment, devices of a fog network may provide for example, computational, storage, and/or network resources in exchange for fungible tokens. In one such variant, the user contributions are recorded in a blockchain data structure, thereby enabling users to be compensated for their contributions of resources to the network at a later time. Unlike traditional networking techniques which often rely on centralized networks directing and/or commandeering user equipment for network bandwidth, the various aspects of the present disclosure are directed to ensuring that crediting and debiting of participation can be performed at the edge of the network (within the fog) without requiring authentication or trust exchanges. More directly, various aspects of the present disclosure are directed to verification and/or validation of work performed by peer devices.

Abstract: Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks, to estimate a bit error rate (BER) of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous to estimate a BER of encoded data, e.g., to facilitate decoding of the encoded data. In this manner, neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by comparing an estimated BER to a threshold (e.g., a threshold BER level) prior to decoding of the encoded data. For example, an additional NN activation indication may be provided, e.g., to indicate that the encoded data may be decoded or to indicate that error present in the encoded data is to be reduced.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data specific to a processing mode selection. 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 a specific processing mode selection. The processing mode selection may include a single processing mode, a multi-processing mode, or a full processing mode. The processing mode selection may be associated with an aspect of a wireless protocol. 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.

Abstract: Methods, apparatuses, and systems related to wireless main memory for computing are described. A device may include a processor that is wirelessly coupled to a memory array, which may be in a physically separate device. The processor may execute instructions stored in and wirelessly communicated from the memory array. The processor may read data from or write data to the memory array via a wireless communication link (e.g., using resources of an ultra high frequency, super high frequency, and/or extremely high frequency band). Several devices may have a small amount of local memory (or no local memory) and may share, via a wireless communication link, a main memory array. Memory devices may include memory resources and transceiver resources; they may be configured to use one or several communication protocols over licensed or shared frequency spectrum bands, directly (e.g., device-to-device) or indirectly (e.g., via a base station).

Abstract: The present disclosure relates to a method and a system for laser distance measurement. The method includes: obtaining, from a control circuit, a synchronization signal; generating, by at least one signal generator, a first periodic signal, a second periodic signal, and a third periodic signal based on the synchronization signal; emitting, by a laser emitting device, a laser beam toward a target, the laser beam being generated under a modulation of the first periodic signal; generating, by an optical detector, a measurement signal in response to a signal mixing of the second periodic signal and a reflected laser beam from the target; and determining a distance to the target based on the measurement signal and the third periodic signal.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data specific to a processing mode selection. 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 a specific processing mode selection. The input data is mixed with coefficient data at layers of multiplication/accumulation processing units (MAC units). The processing mode selection may be associated with an aspect of a wireless protocol. 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.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of configuration modes for baseband units (BBU) and remote radio heads (RRH). For example, a computing system including a BBU and a RRH may receive a configuration mode selection including information indicative of a configuration mode for respective processing units of the BBU and the RRH. The computing system allocates the respective processing units to perform wireless processing stages associated with a wireless protocol. The BBU and/or the RRH may generate an output data stream based on the mixing of coefficient data with input data at the BBU and/or the RRH. 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.

Abstract: Systems, methods, and apparatuses for wireless communication are described. Input data for in-phase branch/quadrature branch (I/Q) imbalance or mismatch may be compensated for or non-linear power amplifier noise may be used to generate compensated input data. In some examples, a transmitter may be configured to transmit communications signaling via a first antenna, the transmitter including a filter configured for digital mismatch correction; a receiver may be configured to receive communications signaling via a second antenna; and a switch may be configured to selectively activate a first switch path to couple the transmitter and the first antenna and a second switch path to couple the receiver and the transmitter to provide communications signaling received via the transmitter as feedback for the filter through the receiver.

Abstract: Methods and apparatus for applying data analytics such as deep learning algorithms to sensor data. In one embodiment, an electronic device such as a camera apparatus including a deep learning accelerator (DLA) communicative with an image sensor is disclosed, the camera apparatus configured to evaluate unprocessed sensor data from the image sensor using the DLA. In one variant, the camera apparatus provides sensor data directly to the DLA, bypassing image signal processing in order to improve the effectiveness the DLA, obtain DLA results more quickly than using conventional methods, and further allow the camera apparatus to conserve power.

Abstract: Systems, apparatuses, and methods related to organizing data to correspond to a matrix at a memory device are described. Data can be organized by circuitry coupled to an array of memory cells prior to the processing resources executing instructions on the data. The organization of data may thus occur on a memory device, rather than at an external processor. A controller coupled to the array of memory cells may direct the circuitry to organize the data in a matrix configuration to prepare the data for processing by the processing resources. The circuitry may be or include a column decode circuitry that organizes the data based on a command from the host associated with the processing resource. For example, data read in a prefetch operation may be selected to correspond to rows or columns of a matrix configuration.

Abstract: Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Abstract: Systems, apparatuses and method related to remotely executable instructions are described. A device may be wirelessly coupled to (e.g., physically separated) another device, which may be in a physically separate device. The another device may remotely execute instructions associated with performing various operations, which would have been entirely executed at the device absent the another device. The outputs obtained as a result of the execution may be transmitted, via the transceiver, back to the device via a wireless communication link (e.g., using resources of an ultra high frequency (UHF), super high frequency (SHF), extremely high frequency (EHF), and/or tremendously high frequency (THF) bands). The another device at which the instructions are remotely executable may include memory resources, processing resources, and transceiver resources; they may be configured to use one or several communication protocols over licensed or shared frequency spectrum bands, directly (e.g.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data specific to a processing mode selection. 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 a specific processing mode selection. The processing mode selection may include a single processing mode, a multi-processing mode, or a full processing mode. The processing mode selection may be associated with an aspect of a wireless protocol. 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.

Abstract: Examples described herein include methods, devices, and systems which may compensate input data for nonlinear power amplifier noise to generate compensated input data. In compensating the noise, during an uplink transmission time interval (TTI), a switch path is activated to provide amplified input data to a receiver stage including a recurrent neural network (RNN). The RNN may calculate an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the power amplifier noise. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the amplified input data may also be transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path may be deactivated and the receiver stage may receive an additional RF wireless transmission to be processed in the receiver stage.

Abstract: Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein may be used to implement error code correction (ECC) decoders.

Abstract: An apparatus is disclosed. The apparatus comprises a plurality of antennas and an integrated circuit chip coupled to the plurality of antennas, and is configured to process cellular signals received from the plurality of antennas in accordance with a cellular communication protocol and to process radio frequency identification (RFID) signals received from the plurality of antennas in accordance with an RFID protocol.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of multiple frequency bands transmission with a recurrent neural network that compensates for the self-interference noise generated by power amplifiers at harmonic frequencies of a respective wireless receiver. The recurrent neural network may be coupled to antennas of a wireless device and configured to generate the adjusted signals that compensate self-interference. The recurrent neural network may include a network of processing elements configured to combine transmission signals into sets of intermediate results. Each set of intermediate results may be summed in the recurrent neural network to generate a corresponding adjusted signal. The adjusted signal is receivable by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same or different frequency band as the wireless receiver is receiving.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of mixing input data with coefficient data specific to a processing mode selection. 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 a specific processing mode selection. The input data is mixed with coefficient data at layers of multiplication/accumulation processing units (MAC units). The processing mode selection may be associated with an aspect of a wireless protocol. 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.