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 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.

Abstract: Examples described herein include apparatuses and methods for full duplex device-to-device cooperative communication. Example systems described herein may include self-interference noise calculators. The output of a self-interference noise calculator may be used to compensate for the interference experienced due to signals transmitted by another antenna of the same wireless device or system. In implementing such a self-interference noise calculator, a selected wireless relaying device or wireless destination device may operate in a full-duplex mode, such that relayed messages may be transmitted as well as information from other sources or destinations during a common time period (e.g., symbol, slot, subframe, etc.).

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.

Abstract: Apparatuses, systems, and methods related to an image processor formed in an array of memory cells are described. An image processor as described herein is configured to reduce complexity and power consumption and/or increase data access bandwidth by performing image processing in the array of memory cells relative to image processing by a host processor external to the memory array. For instance, one apparatus described herein includes sensor circuitry configured to provide an input vector, as a plurality of bits that corresponds to a plurality of color components for an image pixel, and an image processor formed in an array of memory cells. The image processor is coupled to the sensor circuitry to receive the plurality of bits of the input vector. The image processor is configured to perform a color correction operation in the array by performing matrix multiplication on the input vector and a parameter matrix to determine an output vector that is color corrected.

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: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self-interference noise calculator. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate adjusted signals that compensate self-interference. The self-interference noise calculator 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 self-interference noise calculator to generate a corresponding adjusted signal. The adjusted signal is received by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same frequency band as the wireless receiver is receiving.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

Abstract: Methods and apparatus for organizing a distributed network based on routine usage data. In one exemplary embodiment, devices analyze routine usage data via e.g., machine learning and/or other heuristics to determine a set of routine rules and/or routine trigger conditions. Multiple devices may be assigned routine rules and/or routine trigger conditions that establish a fog network. Various described embodiments may incorporate time-shifted control plane operation and/or ledger-based control plane operation; these variants do not require central network management. Other optimizations may include dynamic selection of network topologies and/or routing technologies.

Abstract: A method can include performing at least one glitch resistance operation and detecting, by a circuit included in a glitch detector coupled via a connection matrix to a first processing unit, an indication of a glitch on a memory system. The method can include notifying, via the connection matrix, at least a second processing unit of the detected indication of the glitch. The method can include subsequent to notifying at least the second processing unit, transmitting via the at least the second processing unit a glitch confirmation signal.

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.

Abstract: Apparatuses, systems, and methods related to an image processor formed in an array of memory cells are described. An image processor as described herein is configured to reduce complexity and power consumption and/or increase data access bandwidth by performing image processing in the array of memory cells relative to image processing by a host processor external to the memory array. For instance, one apparatus described herein includes sensor circuitry configured to provide an input vector, as a plurality of bits that corresponds to a plurality of color components for an image pixel, and an image processor formed in an array of memory cells. The image processor is coupled to the sensor circuitry to receive the plurality of bits of the input vector. The image processor is configured to perform a color correction operation in the array by performing matrix multiplication on the input vector and a parameter matrix to determine an output vector that is color corrected.

Abstract: Methods, apparatuses, and systems for repairing defective memory cells in regions of a memory array associated with high or low priority levels are disclosed. A repair address generator may be configured to generate a memory address map for repair (e.g., blowing fuses at a fuse circuit), depending on whether certain applications may operate at a high priority level indicative of a low bit error rate or a low priority level indicative of a higher bit error rate. For example, a specified error rate associated with a low priority level may correspond to a threshold error rate for certain applications, such as a neural network application that stores trained weights. Such neural network applications may access trained weights being partially stored in defective memory cells, with the least significant bits of such trained weights being stored in defective memory cells that are not repaired according to the memory address map.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self-interference noise calculator that compensates for the self-interference noise generated by power amplifiers at harmonic frequencies of a respective wireless receiver. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate the adjusted signals that compensate self-interference. The self-interference noise calculator 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 self-interference noise calculator to generate a corresponding adjusted signal.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

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.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self interference noise calculator. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate adjusted signals that compensate self-interference. The self-interference noise calculator 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 self-interference noise calculator to generate a corresponding adjusted signal. The adjusted signal is received by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same frequency band as the wireless receiver is receiving.

Abstract: Methods, apparatuses, and systems for implementing data flows in a processor are described herein. A data flow manager may be configured to generate a configuration packet for a compute operation based on status information regarding multiple processing elements of the processor. Accordingly, multiple processing elements of a processor may concurrently process data flows based on the configuration packet. For example, the multiple processing elements may implement a mapping of processing elements to memory, while also implementing identified paths, through the processor, for the data flows. After executing the compute operation at certain processing elements of the processor, the processing results may be provided. In speech signal processing operations, the processing results may be compared to phonemes to identify such components of human speech in the processing results.

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.