Micron Technology Incorporated

Lodestar Licensing Group LLC

Tamara Schmitz

Jeremy Chritz

Fa-Long Luo

Jaime Cummins

David Hulton

John L Watson

John Schroeter

Jonathan D Harms

Mark Wang

Kevin T Majerus

Aaron Preston Boehm

Danilo Caraccio

Sujeet V Ayyapureddi

Robert F Gazdzinski

Markus H Geiger

Jaime C Cummins

Angelo Alberto Rovelli

Edmund Gieske

Craig A Jones

Nicolo Izzo

Max Vohra

Jamie Cummins

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.

Glitch detection

Methods and apparatus for biometric data maintenance, access and distribution across two or more experiential and/or network domains. In one embodiment, a 5G NR-based network architecture is provided which allows ultra-low latency and effectively user-imperceptible biometric data use for e.g., authentication and maintenance of user state across multiple domains via multiple constituent user devices (e.g., UEs). The network architecture includes both (i) a distributed biometric database (BDB) model wherein relevant biometric data for individuals/UEs is intelligently cached in various portions of the distributed database, and (ii) centralized and local BAEs (biometric analytics entities) which manage the aforementioned intelligent caching, as well as network configuration using one or both of 5G NR network 'slicing' and CU/DU split options to optimize end-user biometric-related applications such as those providing identification/authentication, AR functions, VR functions or yet others.

Methods and apparatus for persistent biometric profiling

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.

Wireless devices and systems including examples of full duplex transmission

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.

Cooperative learning neural networks and systems

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.).

Full duplex device-to-device cooperative communication

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 may allocate 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 (e.g., New Radio (NR)) wireless communications in a power-efficient and time-efficient manner.

Wireless devices and systems including examples of configuration modes for baseband units and remote radio heads

Methods, systems, and devices for verification of a volatile memory, such as a dynamic random-access memory (DRAM), using a unique identifier (ID) are described. A memory device may store a unique ID for a DRAM component of the memory device in non-volatile memory (e.g., in the DRAM, external to the DRAM). A host device coupled with the memory device may store, to non-volatile memory at the host device, information for verifying the identity of the DRAM component, for example, based on the unique ID. The memory device and host device may perform a procedure for verification of the identity of the DRAM component using the unique ID of the DRAM and the verification information stored at the host device. If the host device detects that the DRAM has been replaced or modified based on the verification procedure, the host device may disable one or more features of the memory device.

Verification of a volatile memory using a unique identifier

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.

Wireless devices and systems including examples of cross correlating wireless transmissions

A system and method detect a row hammer attack on the memory media device and generates a hardware interrupt based on the detection of the row hammer attack. This row hammer interrupt is communicated to an operating system of a host computing device, which in turn performs an interrupt service routine including generating a command to perform a row hammer mitigation operation. This command is provided to the memory controller which performs the row hammer mitigation operation in response to the command such as activating victim row(s) of the memory media device or throttling data traffic to the memory media device.

Row hammer interrupts to the operating system

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.

Wireless devices and systems including examples of mixing coefficient data specific to a processing mode selection

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 devices and methods which may facilitate tensor memory access

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.

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

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.

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.

Growing community of inventors

Scotts Valley, CA, United States of America

Tamara Schmitz