Abstract: Apparatuses and methods can be related to implementing age-based network training. An artificial neural network (ANN) can be trained by introducing errors into the ANN. The errors and the quantity of errors introduced into the ANN can be based on age-based characteristics of the memory device.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. An integrated circuit may be configured to perform at least computations on matrix operands and configured with: random access memory configured to store instructions executable by the Deep Learning Accelerator and store matrices of an Artificial Neural Network; a connection between the random access memory and the Deep Learning Accelerator; and an interface to a memory controller. The interface may be configured to facilitate access to the random access memory by the memory controller. In response to an indication provided in the random access memory, the Deep Learning Accelerator may execute the instructions to apply input that is stored in the random access memory to the Artificial Neural Network, generate output from the Artificial Neural Network, and store the output in the random access memory.

Abstract: Systems, methods, and apparatus related to memory devices such as solid state drives. In one approach, data is received from a host system (e.g., data to be written to an SSD). The received data is encoded using a first error correction code to generate first parity data. A temperature at which memory cells of a storage device (e.g., the SSD) will store the received data is determined. In response to determining the temperature, a first portion of the received data is identified (e.g., data in memory storage that is error-prone at a predicted higher temperature that has been determined based on output from an artificial neural network using sensor(s) input). The identified first portion is encoded using a second error correction code to generate second parity data. The second error correction code has a higher error correction capability than the first error correction code. The encoded first portion, the first parity data, and the second parity data are stored in the memory cells of the storage device.

Abstract: Methods, systems, and devices for deep learning access and authentication in a computing architecture are described. A computing system a processor, a deep learning device, and a memory system. The deep learning device may be operable to perform operations associated with the processor using a neural network. The memory system enable or disable access to the deep learning device by the processor. For example, the memory system may verify whether the processor is authorized to access the deep learning device and enable or disable access based on the verification. If access is enabled, the deep learning device may perform the one or more operations associated with the processor. If access is disabled, the deep learning device may be restricted from performing the one or more operations associated with the processor.

Abstract: Systems, methods and apparatus of predictive maintenance of automotive tires. For example, a vehicle has: a tire and wheel assembly; one or more sensors configured on the tire and wheel assembly to measure operating parameters of the tire and wheel assembly; an artificial neural network configured to analyze the operating parameters of the tire and wheel assembly as a function of time to generate a result; and at least one processor configured to generate a suggestion for a maintenance service of the tire and wheel assembly based on the result from the artificial neural network analyzing the operating parameters of the tire and wheel assembly. For example, the sensors can be configured to measure a pressure of air in the tire, a temperature of the air in the tire, a speed of spinning of the wheel, a traction force or torque applied by the wheel on an axle of the vehicle, and/or vibration of the wheel.

Abstract: A dual-port, dual function memory device can be configured to perform operations on data written to the memory device using artificial intelligence (AI) circuitry, such as a neuromorphic array and/or a deep learning accelerator (DLA), of the memory device. The memory device can include a port dedicated for communication between the AI circuitry and a host device and another port dedicated for communication between a memory array of the memory device and a host device. Performing operations, such as image processing operations, using AI circuitry of a memory device can reduce data transfers, reduce resource consumption, and offload workloads from a host device.

Abstract: Systems, methods and apparatuses of predictive maintenance of automotive transmission of vehicles. For example, the transmission has at least one sensor to measure a temperature in transmission fluid, the torque applied on a shaft of the transmission, a vibration sensor, and/or a microphone. During a period in which the vehicle is assumed to be operating normally, the sensor data generated by the transmission sensor(s) is used to train an artificial neural network to recognize the normal patterns in the sensor data. Subsequently, the trained artificial neural network is used to determine whether the current sensor data from the transmission sensor(s) are abnormal. A maintenance alert can be generated for the vehicle in response to a determination that the operations of the transmission are abnormal according to the artificial neural network and the current sensor data.

Abstract: Systems, methods and apparatuses to detect an item left in a vehicle and to generate an alert about the item. For example, a camera configured in a vehicle can be used to monitor an item associated with a user of the vehicle. The item as in an image from the camera can be identified and recognized using an artificial neural network. In response to a determination that the item recognized in the image is left in the vehicle after the user has exited the vehicle, an alert is generated to indicate that an item is in the vehicle but the user is leaving the vehicle.

Abstract: In some implementations, an extended reality (XR) device may generate a first event in a virtual environment, wherein the first event is associated with a first difficulty level. The XR device may detect vital information of a user associated with the XR device, wherein the vital information is associated with the first event. The XR device may determine whether the vital information satisfies a threshold. The XR device may transmit, to a server, an indication that indicates whether the vital information satisfies the threshold. The XR device may receive, from the server, virtual environment information, wherein the virtual environment information is based on whether the vital information satisfies the threshold. The XR device may generate, based on the virtual environment information, a second event in the virtual environment, wherein the second event is associated with a second difficulty level.

Abstract: A solid state drive having a drive aggregator and multiple component solid state drives. Different component solid state drives in solid state drive are configured with different optimizations of memory/storage operations. An address map in the solid state drive is used by the drive aggregator to host different namespaces in the component solid state drives based on optimization requirements of the namespaces and based on the optimizations of memory operations that have been implement in the component solid state drives.

Abstract: A memory device can be configured to receive data, via a first port, from an image sensor coupled thereto. The memory device can be further configured to perform an image processing operation on the data. The image processing operation can be performed using logic circuitry of the memory device. The memory device can be configured to transmit operated-on data from the memory device to image signal processing (ISP) circuitry via a second port of the memory device. Pulling data directly from an image sensor, via a memory device, can reduce data transfers, reduce resource consumption, and offload workloads from ISP circuitry, a host device, and/or a host processing device, for example.

Abstract: Systems, methods and apparatus of optimizing neural network computations of predictive maintenance of vehicles. For example, a data storage device of a vehicle includes: a host interface configured to receive a sensor data stream from at least one sensor configured on the vehicle; at least one storage media component having a non-volatile memory; and a controller. The non-volatile memory is configured into multiple partitions (e.g., namespaces) having different sets of memory operation settings configured for different types of data related to an artificial neural network (ANN). The partitions include an output partition configured to store output data from the ANN. The sensor data stream is applied in the ANN to predict a maintenance service of the vehicle. The memory units of the input partition can be configured for cyclic sequential overwrite of selected outputs that are updated less frequently than inputs to the ANN.

Abstract: A memory component includes a memory region to store a machine learning model and input data and another memory region to store host data from a host system. A controller can be coupled to the memory component and can include in-memory logic to perform a machine learning operation by applying the machine learning model to the input data to generate an output data. A bus can receive additional data from the host system and a decoder can receive the additional data from the bus and can transmit the additional data to the other memory region or the in-memory logic of the controller based on a characteristic of the additional data.

Abstract: A solid state drive having a drive aggregator configured to interface with a host system, and a plurality of component solid state drives connected to the drive aggregator. Each of the component solid state drives has a controller capable of processing commands from host systems. The drive aggregator is configured to receive commands from the host system and transmit commands to the component solid state drives to implement the commands received from the host system.

Abstract: A solid state drive having a drive aggregator configured with multiple host interfaces for parallel and/or redundant connections to one or more host systems. The solid state drive has a plurality of component solid state drives connected to the drive aggregator. Each of the component solid state drives has a controller capable of processing commands from host systems. The drive aggregator is configured to receive commands in the host interfaces concurrently and implement the commands received from the host system using the plurality of component solid state drives.

Abstract: Systems, methods and apparatus of accelerating neural network computations of predictive maintenance of vehicles. For example, a data storage device of a vehicle includes: a host interface configured to receive a sensor data stream from at least one sensor configured on the vehicle; at least one storage media component having a non-volatile memory to store at least a portion of the sensor data stream; a controller; and a neural network accelerator coupled to the controller. The neural network accelerator is configured to perform at least a portion of computations based on an artificial neural network and the sensor data stream to predict a maintenance service of the vehicle.

Abstract: A method comprising receiving, at a memory sub-system from a host system, receiving, at one or more configuration parameters reflecting an expected type of use of the memory sub-system; receiving one or more environmental parameters of the memory sub-system, wherein the environmental parameters reflect characteristics of an environment of the memory sub-system; and selecting a programming operation parameter to be utilized by the memory sub-system based on the configuration parameters and environmental parameters.

Abstract: Systems, devices, and methods related to video analysis using an Artificial Neural Network (ANN) are described. For example, a data storage device can be configured to perform the computation of an ANN to recognize or classify features captured in the video images. The recognition or classification results of a prior video frame can be used to accelerate the analysis of the next video frame. The ANN can be organized in layers, where the intermediate result of a current layer can be further analyzed by a next layer for improved accuracy and confidence level. Before or while processing using the next layer, the intermediate result can be compared to the results obtained for the prior frame. If, in view of the results of the prior frame, the confidence level of the intermediate result is boosted to above a threshold, the subsequent layer(s) can be skipped or terminated early.

Abstract: Methods, systems, and devices for techniques to season memory cells are described. A memory device may receive a command to season the memory device from a device configured to season the memory device or from a host device. Based on receiving the command, the memory device may identify a quantity of cycles to season the memory device based on receiving the command. In one case, the memory device may identify the quantity of cycles based on the command including an indication of the quantity of cycles used to season the memory device. In another case, the memory device may identify the quantity of cycles based on the command including one or more parameters associated with operating the memory device. In either case, the memory device may execute the quantity of cycles and indicate a completion of seasoning the memory device based on executing the quantity of cycles.

Abstract: Methods, systems, and devices for operating memory die based on temperature data are described. A memory system may include a set of temperature sensors each corresponding to one of a set of memory dies. A controller at the memory system may receive, from the set of temperature sensors, temperatures measured at the set of memory dies. Then the controller may identify that an operation of a first memory die is associated with an increased likelihood of errors based on the temperature measured at the first memory die. In response, the controller may adjust a parameter for operating the first memory die from a first value to a second value associated with a decreased operating temperature and operate the first memory die according to the second value of the parameter while operating one or more of the other memory dies in the set according to the first value of the parameter.