Abstract: Matrix multiplication process is segregated between two separate dies—a memory die and a compute die to achieve low latency and high bandwidth artificial intelligence (AI) processor. The blocked matrix-multiplication scheme maps computations across multiple processor elements (PE) or matrix-multiplication units. The AI architecture for inference and training includes one or more PEs, where each PE includes memory (e.g., ferroelectric (FE) memory, FE-RAM, SRAM, DRAM, MRAM, etc.) to store weights and input/output I/O data. Each PE also includes a ring or mesh interconnect network to couple the PEs for fast access of information.

Abstract: Methods, systems, and devices for wireless communications are described. A network entity may obtain a first message indicating one or more artificial intelligence (AI)/machine learning (ML) capabilities of a user equipment (UE) and may further obtain, from an AI/ML service, a second message indicating one or more AI/ML service capabilities of the AI/ML service. The network entity may output a control message indicating one or more cross-node AI/ML configurations based on the one or more AI/ML capabilities of the UE and the one or more AI/ML service capabilities of the AI/ML service. The one or more cross-node AI/ML configurations may configure AI/ML functions of the UE for use with the AI/ML service. In some examples, life cycle management (LCM) procedures may be triggered by a UE, and the UE may transmit signaling associated with the LCM procedures (e.g., a request for LCM signaling or an indication of LCM).

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a primary cell or a primary secondary cell (PSCell), configuration information for a conditional PSCell addition or change (CPAC), the configuration information indicating configurations for one or more candidate PSCells. The UE may detect a failure, associated with the conditional PSCell addition or change, associated with a first target PSCell from the one or more candidate PSCells. The UE may perform an action to recover the CPAC based at least in part on detecting the failure. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Abstract: A device structure comprises a first conductive interconnect, an electrode structure on the first conductive interconnect, an etch stop layer laterally surrounding the electrode structure; a plurality of memory devices above the electrode structure, where individual ones of the plurality of memory devices comprise a dielectric layer comprising a perovskite material. The device structure further comprises a plate electrode coupled between the plurality of memory devices and the electrode structure, where the plate electrode is in direct contact with a respective lower most conductive layer of the individual ones of the plurality of memory devices. The device structure further includes an insulative hydrogen barrier layer on at least a sidewall of the individual ones of the plurality of memory devices; and a plurality of via electrodes, wherein individual ones of the plurality of via electrodes are on a respective one of the individual ones of the plurality of memory devices.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Abstract: Various implementations for an automatic system and processes for monitoring the use of personal protective equipment in a work space are provided. The system includes barcodes attached to the personal protective equipment. The operation of the system involves capturing images of the work space and detecting human form objects and barcode objects in the images. The system and processes further involve calculating the probability that a user is wearing all personal protective equipment in accordance with safety rules applicable to the work space. In some implementations, the system and processes may be used to track personal protective equipment and other objects in a work space. Accordingly, the system and processes may be used to prevent or limit the occurrence of accidents, incidents and/or injuries in hazardous work environments.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain an indication to perform a selective secondary cell group activation or a subsequent conditional handover associated with a subsequent conditional primary cell change, the subsequent conditional handover associated with the subsequent conditional primary cell change being performed after a previous conditional handover associated with a previous conditional primary cell change. The UE may initiate the selective secondary cell group activation or the subsequent conditional handover. The UE may detect a failure occurrence associated with the selective secondary cell group activation or the subsequent conditional handover.

Abstract: Certain aspects of the present disclosure provide techniques for data collection for non-terrestrial networks (NTN). One aspect provides a method for wireless communications by a user equipment (UE). The method generally includes transmitting an indication of a capability of the UE to connect to a network via both terrestrial network (TN) cells and non-terrestrial network (NTN) cells and transmitting one or more data collection reports in accordance with the indicated capability.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements for RRC IDLE and INACTIVE state operations. In a first aspect, a method of wireless communication includes receiving, by a wireless communication device, artificial intelligence (AI) model configuration information for IDLE/INACTIVE state procedures; retrieving, by the wireless communication device, an AI model for IDLE/INACTIVE state procedures based on the AI model configuration information; and performing, by the wireless communication device, one or more IDLE/INACTIVE state procedures based on the AI model. Other aspects and features are also claimed and described.

Abstract: A packaging technology to improve performance of an AI processing system resulting in an ultra-high bandwidth system. An IC package is provided which comprises: a substrate; a first die on the substrate, and a second die stacked over the first die. The first die can be a first logic die (e.g., a compute chip, CPU, GPU, etc.) while the second die can be a compute chiplet comprising ferroelectric or paraelectric logic. Both dies can include ferroelectric or paraelectric logic. The ferroelectric/paraelectric logic may include AND gates, OR gates, complex gates, majority, minority, and/or threshold gates, sequential logic, etc. The IC package can be in a 3D or 2.5D configuration that implements logic-on-logic stacking configuration. The 3D or 2.5D packaging configurations have chips or chiplets designed to have time distributed or spatially distributed processing. The logic of chips or chiplets is segregated so that one chip in a 3D or 2.5D stacking arrangement is hot at a time.

Abstract: A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

Abstract: A process integration and patterning flow used to pattern a memory array area for an embedded memory without perturbing a fabricating process for logic circuitries. The fabrication process uses a pocket mask (e.g., a hard mask) to decouple the etching process of a memory array area and non-memory area. Such decoupling allows for a simpler fabrication process with little to no impact on the current fabrication process. The fabrication process may use multiple pocket masks to decouple the etching process of the memory array area and the non-memory area. This fabrication process (using multiple pocket masks) allows to avoid exposure of memory material into a second pocket etch chamber. The process of etching memory material is decoupled from the process of etching an encapsulation material. Examples of embedded memory include dynamic random-access memory and ferroelectric random-access memory.

Abstract: Aspects described herein relate to receiving, from a network node, a list of supported models or model structures (MS) identifiers (IDs) per machine learning function name (MLFN) or machine learning feature (MLF) at the network node, updating a capability at the UE to an updated capability based on the list of supported models or MS IDs per MLFN or MLF at the network node, and downloading, at the UE and from a model repository, one or more models or MSs per MLFN or MLF based on the updated capability and available resources at the UE. Other aspects relate to transmitting the list of supported models or MS IDs and configuring use of a model or MS ID for a particular MLFN or MLF.

Abstract: Described is a low power, high-density non-volatile differential memory bit-cell. The transistors of the differential memory bit-cell can be planar or non-planer and can be fabricated in the frontend or backend of a die. A bit-cell of the non-volatile differential memory bit-cell comprises first transistor first non-volatile structure that are controlled to store data of a first value. Another bit-cell of the non-volatile differential memory bit-cell comprises second transistor and second non-volatile structure that are controlled to store data of a second value, wherein the first value is an inverse of the second value. The first and second volatile structures comprise ferroelectric material (e.g., perovskite, hexagonal ferroelectric, improper ferroelectric).

Abstract: This disclosure pertains to systems and methods to detect network errors in a parallel redundant protocol (PRP) network. A node-level redundancy error subsystem of an intelligent electronic device (IED) maintains records (e.g., counts) of information associated with missing duplicate frames expected from node devices on the PRP network via redundant first and second local area networks (LANs). Non-zero counts of missing duplicate frames may be identified as network errors.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may configure a user equipment (UE) to report applicability information associated with artificial intelligence (AI) and/or machine learning (ML) functionalities and/or models. The applicability information indicates an applicability of a functionality and/or model per AI/ML-enabled feature or feature group. Based on the applicability information and a handover of the UE from a source network entity to a target network entity, the target network entity may configure a functionality and/or model. Additionally, or alternatively, the UE may be configured with a reference configuration and one or more additional configurations corresponding to respective AI/ML functionalities and/or models. The UE may apply an additional configuration based on a handover to the target network entity, the additional configuration satisfying an applicability condition, or both.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network node, a request for network assisted information associated with one or more of a machine learning (ML) model or analytics, the ML model or the analytics being associated with an application layer communication that is associated with the UE. The UE may receive, from the network node, one or more of the ML model or the analytics. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may monitor a performance of a machine learning (ML) model or ML model-based functionality associated with a user equipment (UE). The UE may receive one or more control messages that indicate a life cycle management (LCM) operation for the ML model or ML model-based functionality. The one or more control messages may include an indication of whether the LCM operation is based on the performance of the MIL model or ML model-based functionality. In some examples, the indication may include or be an example of a performance report associated with the performance of the ML model or ML model-based functionality. The UE may perform the LCM operation for the ML model or ML model-based functionality. The UE or the network entity may transmit the indication to a server associated with the ML model or ML model-based functionality.

Abstract: An apparatus and configuring scheme where a capacitive input circuit can be programmed to perform different logic functions by adjusting the switching threshold of the capacitive input circuit. Digital inputs are received by respective capacitors on first terminals of those capacitors. The second terminals of the capacitors are connected to a summing node. A pull-up and pull-down device are coupled to the summing node. The pull-up and pull-down devices are controlled separately. During a reset phase, the pull-up and/or pull-down devices are turned on or off in a sequence, and inputs to the capacitors are set to condition the voltage on node n1. As such, a threshold for the capacitive input circuit is set. After the reset phase, an evaluation phase follows. In the evaluation phase, the output of the capacitive input circuit is determined based on the inputs and the logic function configured during the reset phase.