8 results about "Application specific" patented technology

In one embodiment, a non-transitory computer-readable media stores instructions executable by processors for generating a prompt configured for eliciting outputs from large language models (LLMs) based on information associated with a task, inputting the prompt to a first LLM configured to output a response based on processing the prompt, determining metrics for evaluating the first LLM based on the task, wherein each of the metrics is associated with a scoring guideline, generating metric prompts based on the respective metrics and the scoring guidelines associated with the respective metrics, inputting the response and the metric prompts to second LLMs configured to output scores corresponding to the respective metrics based on processing the response and the metric prompts, and generating an analysis report based on the metrics and their corresponding scores.

This application discloses overhanging cache stacked chiplets on glass core. A three-dimensional L3 cache producing overhanging cache chiplet architecture. Examples stack cache dies on caches in a system die and hybrid bond (HB) them together, producing a "T" shaped assembly attached to a glass core interposer via a field of solder microbumps. The coefficient of thermal expansion (CTE) of the glass core interposer can be tailored in an application specific manner.

Methods and systems are described for enhanced, touch enabled graphical user interfaces optimized for mobile devices. The mobile device may comprise a display with a capacitive array. An application specific application circuit may comprise Processing Elements (PEs), which are configured to perform operations comprising matrix multiplications, convolutions, and activation functions. The PEs may be organized in a grid or array format.

Methods and systems are described for enhanced, touch enabled graphical user interfaces optimized for mobile devices. The mobile device may comprise a display with a capacitive array. An application specific application circuit may comprise Processing Elements (PEs), which are configured to perform operations comprising matrix multiplications, convolutions, and activation functions. The PEs may be organized in a grid or array format.

User equipment (UE) comprises a means for collecting one or more energy related metrics of the UE during a session. The UE determines the energy consumption by the UE or that a quality of experience of the UE is to be changed based on the collected energy related metrics. The UE takes one or more actions in response to determining that energy consumption by the UE or the quality of experience of the UE is to be changed. The session may be a media streaming session, an internet protocol multimedia system session, or an extended reality (XR) session. The determination may be the energy consumption is to be reduced. The energy related metrics may be device specific metrics (e.g. time duration the screen is on and the energy consumption of the screen, and then the same with camera, sensors and speakers etc.), application specific metrics (e.g. energy consumption of the application), or processing specific metrics (e.g. energy consumption of radio transmissions or the encoding, decoding, split-rendering etc.).The means for taking actions may involve the changing of settings (e.g. changing display settings).

Methods and systems are described for enhanced, touch enabled graphical user interfaces optimized for mobile devices. The mobile device may comprise a display with a capacitive array. An application specific application circuit may comprise Processing Elements (PEs), which are configured to perform operations comprising matrix multiplications, convolutions, and activation functions. The PEs may be organized in a grid or array format.

In one embodiment, a non-transitory computer-readable media stores instructions executable by processors for generating a prompt configured for eliciting outputs from large language models (LLMs) based on information associated with a task, inputting the prompt to a first LLM configured to output a response based on processing the prompt, determining metrics for evaluating the first LLM based on the task, wherein each of the metrics is associated with a scoring guideline, generating metric prompts based on the respective metrics and the scoring guidelines associated with the respective metrics, inputting the response and the metric prompts to second LLMs configured to output scores corresponding to the respective metrics based on processing the response and the metric prompts, and generating an analysis report based on the metrics and their corresponding scores.

In various examples, each hosted application may be modeled with a corresponding application-specific resource consumption model that predicts a measure of that application's anticipated resource utilization at some future time based on an input representation of one or more features of the current state of an instance of the hosted application. For cloud gaming, those features may include the current level being played, current obstacles, user results playing the level or obstacles, metadata quantifying one or more aspects of the level or obstacles, game progress, etc. As such, application-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and / or augmented reality (AR) streaming applications, and / or other application types.