Maestro® – Balancing sensory quality in response to latency and user-level choices, in real-time
A Synchronization System
Maestro, a synchronization system that uses an automatic-tuning approach to balancing sensory-quality (such as visual detail or audio fidelity) with responsiveness to user input and latency of changes. Maestro can use a user’s computer language output format (Essence Atoms) to detail a series of preferred tradeoffs in any specific sensory art bank or simulation chronicle.
What is a Sensory Art Bank?
A sensory art bank can be a small rectangular area on a single screen showing a movie or web page or a five-screen wide view of a vast three-dimensional scene or an image on the shirt of a Lego figure inside that scene. For each sensory art bank, there is a tradeoff in how detailed the image or sound or motion or shape (for collision) will be versus how rapidly it responds to changes.
For example, a three-dimensional simulation or high-definition movie that natively generates a new image at 24 times per second (24 Fps) can be synchronized and parallel processed to show a changed visual at 60 times a second (temporal upsampling) or have its pixel density increased to an 8K source (spatial upsampling). If presenting the three-dimensional simulation at 8K requires more processing resources than is available, then the 3D simulation may only be updated at 12 times per second, but the quality will be high, whereas at 60 times per second the quality might be low, but the image is highly interactive.
Maestro automates the tuning of controls for visual, audio, physics-simulation, or other sensory and/or calculation based services.
Traditionally, simulations, video games, and general operating systems have controls that computer-savvy users can to tweak to achieve desired results. Applying such preferences to a range of sensory phenomena can become a challenge, especially if the user wants different tradeoffs in different areas, such as crisp but slow-updating text compared to smooth but fast-updated background visuals.
Each element of a video stream (such as a sensory art bank of visuals spread across many screens, many sensory art banks of sounds across speakers, or various physics simulations across space time scenes) can be affected differently. This functionality can be exposed to users as areas-of-focus versus areas-of-less focus.
For example, the window or region of visuals receiving active input can be identified as an area of focus while areas with no immediate user-driven changes can be identified as areas of less focus.
Maestro can use estimates, scores, and course corrections to slowly tune features to keep user response rate, visuals update rate, sound fidelity, physics simulation precision, and general calculation precision in line as close to desired results as possible.
Dealing with Excess Demand
In any scenario, a user can easily overwhelm the available computational resources by simulating too many physical collisions in one time step or by drawing too many details. Maestro can manage this excess demand for computational resources by turning down all tunable tradeoff options and/or pausing the generation of new content until further input is received. This can help preventing stalls or denial-of-service issues caused by the excess demand.
Overall, Maestro can function as a computational governor that helps manage user intent in real time to deliver the optimal experience for a given set of tasks and computational resources. Probabilistic techniques to help determine the cores used, and therefore select where a task is executed. Probabilistic techniques can be used to help determine the instruction-sets initially chosen. The algorithm-units used to generate the actual instructions can rely on probability tables to choose the more likely outcomes. The methods used to assign a likelihood or probability that something may occur, and thus influence decision making, do not need to be coupled with the probability generating methods that produce random values to use in cryptography, visualization, math-solving (such as Monte Carlo solutions), and other approaches.