Abstract
The University of Central Florida invention is an adaptive system that enables adjustable ranges for parameters and intermediary variables while greatly reducing computer power consumption. The invention determines the adjustable ranges based on the statistics of the parameters/variables during the adaptation process, which reduces errors caused by clipping or low precision and offers improved performance in range-limited adaptive systems.
Unlike other adaptive systems that rely on floating-point numbers for all or a portion of their parameters, the UCF invention uses fixed-point data types and does not use floating-point numbers. In one example application, the UCF adaptive system can effectively train artificial neural networks (ANNs). A class of adaptive systems, ANNs transform inputs (such as text, audio, images, and videos) to outputs of desired formats to achieve various tasks.
Technical Details: The UCF invention comprises an adaptive system (such as an adaptive control system, an adaptive proportional-integral-derivative (PID) controller, or an ANN) and a novel method for adjusting a parameter in the system. For instance, a parameter (fixed-point or analog) with a finite range is adjusted based on a difference between the output signal and a target output signal. Typically, the parameter is an analog electrical signal, a digital electrical signal, an analog optical signal, a digital optical signal, or a digital-and-analog hybrid signal.
Finite ranges of individual parameters are adjusted based on the statistics of the parameter values. For an individual parameter, the statistical distribution can be estimated from the historical values of the parameters during the adaptation process. As an example, if the adaptive system employs discrete, iterative feedback steps, such as those in a reinforcement learning system, the distribution can be fitted from the parameter values from the previous iterations. In another example, if the adaptive system employs continuous feedback, such as a real-time motion system, the distribution can be fitted from a portion of or the whole time series of the parameter in the past.
Partnering Opportunity: The research team is seeking partners for licensing, research collaboration, or both.
Stage of Development: Prototype available.
Benefit
Supports many applications in control and signal processing Enables improved performance in range-limited adaptive systemsMinimizes errors in the target outputCan significantly reduce power consumption compared to floating-point counterpartsMarket Application
Industrial sensing and imagingReal-time PID motion systemsGaming systems
Brochure
