Abstract: In some implementations, an extended reality (XR) device may receive, via an interface of the XR device, an indication of items to be purchased. The XR device may identify one or more physical retail stores that carry the items. The XR device may identify one or more recommended items based on the theme. The XR device may provide, via the interface, a list of the one or more physical retail stores. The XR device may detect that the XR device is within a physical retail store, of the one or more physical retail stores, based on a geographic location associated with the XR device. The XR device may provide, via the interface, an in-store navigation path to direct a user of the XR device via overlayed audio-visual cues to locations within the physical retail store at which the items are held.

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: In some implementations, an extended reality (XR) device may receive, via an interface of the XR device, an input associated with meals of a user associated with the XR device. The XR device may determine, based on the input, a meal plan for the user of the XR device, wherein the meal plan is associated with target meals. The XR device may determine, based on recipes for the target meals, a list of food items for preparing the target meals associated with the meal plan. The XR device may provide, via the interface, the list of food items and the target meals. The XR device may provide, via the interface, an in-store navigation path to direct the user of the XR device via overlayed audio-visual cues to locations within a physical retail store to pick up the food items.

Abstract: In some implementations, an extended reality (XR) device may detect, using a scene captured by the XR device, text associated with a document, wherein the text associated with the document is within a field of view of the XR device. The XR device may determine one or more keywords of the text and a context associated with the text. The XR device may generate, using a language model, predicted text based on the one or more keywords of the text and the context associated with the text, wherein the predicted text is related to the text associated with the document. The XR device may provide, via an interface of the XR device, the predicted text as a visual overlay to the text associated with the document, wherein the predicted text is visually overlayed in proximity to the text associated with the document.

Abstract: In some implementations, an extended reality (XR) device may receive, from a server, a request to resolve an item that is misplaced in a physical retail store, wherein the request indicates a misplaced location associated with the item. The XR device may provide, via an interface, a notification of the request, wherein the notification includes an option to accept the request. The XR device may receive, via the interface, an indication that the request has been accepted. The XR device may transmit, to the server, the indication that the request has been accepted. The XR device may provide, via the interface, an in-store navigation path to direct a user of the XR device via overlayed audio-visual cues to the misplaced location to pick up the item.

Abstract: In some implementations, an extended reality (XR) device may detect, using a camera of the XR device, a clothing item, wherein the clothing item is associated with an identifier. The XR device may transmit, to a server, a request that indicates the identifier. The XR device may receive, from the server, metadata associated with the clothing item, wherein the metadata is associated with the identifier. The XR device may retrieve, from the server, a three-dimensional model of a user associated with the XR device. The XR device may generate a three-dimensional model of the user wearing the clothing item using the three-dimensional model of the user and the metadata. The XR device may provide, via an interface of the XR device, the three-dimensional model of the user wearing the clothing item.

Abstract: Methods, systems, and apparatus related to dynamic distribution of an artificial neural network among multiple processing nodes based on real-time monitoring of a processing load on each node. In one approach, a server acts as an intelligent artificial intelligence (AI) gateway. The server receives data regarding a respective operating status for each of monitored processing devices. The monitored processing devices perform processing for an artificial neural network (ANN). The monitored processing devices each perform processing for a portion of the neurons in the ANN. The portions are distributed in response to monitoring the processing load on each processing device (e.g., to better utilize processing power across all of the processing devices).

Abstract: A solid state drive having a drive aggregator and a plurality of component solid state drive, including a first component solid state drive and a second component solid state drive. The drive aggregator has at least one host interface, and a plurality of drive interfaces connected to the plurality of component solid state drives. The drive aggregator is configured to generate, in the second solid state drive, a copy of a dataset that is stored in the first component solid state drive. In response to a failure of the first component solid state drive, the drive aggregator is configured to substitute a function of the first component solid state drive with respect to the dataset with a corresponding function of the second component solid state drive, based on the copy of the dataset generated in the second component solid state drive.

Abstract: A device having a plurality of pins configured to connect circuits within an integrated circuit package to circuits outside of the integrated circuit package. A driver enclosed within the package is programmable to generate a spread spectrum signal to represent data being transmitted from a pin of the device. Frequency distribution of the signal spreading over a bandwidth in a frequency domain can be programmed to customize the electromagnetic emission caused by the communication of data through the pin. The frequency spreading can be programmed to reduce energy consumption, electromagnetic interference, and/or errors in receiving the data transmitted via the pin. The settings can be programmed into registers enclosed in the integrated circuit package to control the driver and/or dynamically adjusted using an artificial intelligent engine to optimize a cost function.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. An integrated circuit may be configured to execute instructions with 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; a first interface to a memory controller of a Central Processing Unit; and a second interface to a direct memory access controller. While the Deep Learning Accelerator is using the random access memory to process current input to the Artificial Neural Network in generating current output from the Artificial Neural Network, the direct memory access controller may concurrently load next input into the random access memory; and at the same time, the Central Processing Unit may concurrently retrieve prior output from the random access memory.

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.