There is provided a system including a platform controller for managing artificial intelligence services, wherein the system includes a processor coupled with a memory, having stored thereon instructions. The instructions, when executed by the processor, configure the platform controller to receive an artificial intelligence (AI) service registration request from an AI controller controlling the AI service, the AI service registration request including information indicative of locations of the AI service and transmit an AI service registration response to the AI controller, the AI service registration response including routing information at least in part specifying how to reach a coordinator associated with the AI service, the coordinator corresponding to a location of the AI service and transmit a notification indicative of availability of the AI service to a device. When a request for access to the AI service is received from the device, the platform controller is configured to transmit a response to the device, wherein the response is indicative of whether the request is accepted.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a UE capability for AI/ML. A UE may receive a request from a network to report a UE capability for at least one of an AI procedure or an ML procedure. The UE may transmit to the network, based on the request to report the UE capability, an indication of one or more of an AI capability, an ML capability, a radio capability associated with the at least one of the AI procedure or the ML procedure, or a core network capability associated with the at least one of the AI procedure or the ML procedure.

An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.

Techniques for the suggestion and completion of deep learning models are disclosed including receiving a set of data and determining at least one property of the data. A plurality of characteristics of a computing device and a plurality of deep learning models are received and a score for each of the plurality of deep learning models is determined based on the received computing device characteristics and the determined at least one property of the data. The plurality of deep learning models are ranked for presentation to a user based on the determined scores. One or more of the deep learning models are presented on a display based on the ranking. A selection of one of the deep learning models is received and the selected deep learning model is trained using the set of data.

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.

The present disclosure provides a method comprising: receiving an input of a geolocation for detection of one or more target objects within an area; retrieving data related to the one or more target objects from a historical detection module, the historical detection module having performed the following: analysis of a plurality of content streams for a plurality of target objects, detection of the plurality of target objects within one or more frames of the plurality of content streams within the predetermined area, and storage of data related to the detected plurality of target objects, aggregating the retrieved data related to the one or more target objects with the following: weather information of the predetermined area, and locational orientation of the user; and predicting, based on the aggregated data, one or more predictions of the geolocation and timeframe for detection of the one or more target objects within the predetermined area.

As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.

An electronic device includes a memory that stores input matrices A and B, a cache memory, and a processor. The processor generates a compiled representation that includes values for acquiring data from input matrix A when processing instances of input data through the neural network, the values including a base address in input matrix A for each thread from among a number of threads and relative offsets, the relative offsets being distances between elements of input matrix A to be processed by the threads. The processor then stores, in the local cache memory, the compiled representation including the base address for each thread and the relative offsets.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. In some embodiments, the graph includes nodes representing options for implementing each layer of the machine-trained network and edges between nodes for different layers representing different implementations that are compatible. The compiler of some embodiments is also responsible for generating instructions relating to shutting down (and waking up) memory units of cores. In some embodiments, the memory units to shutdown are determined by the compiler based on the data that is stored or will be stored in the particular memory units.

Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.