首页|Findings in Machine Learning Reported from University of Tennessee (Machine lear ning-powered compact modeling of stochastic electronic devices using mixture den sity networks)
Findings in Machine Learning Reported from University of Tennessee (Machine lear ning-powered compact modeling of stochastic electronic devices using mixture den sity networks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Tennessee by NewsRx editors, research stated, "The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to ac curately account for the inherent stochastic nature of certain devices." Our news reporters obtained a quote from the research from University of Tenness ee: "While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capturing the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional m odeling techniques by harnessing the power of machine learning, specifically Mix ture Density Networks (MDNs), to faithfully represent and simulate the stochasti c behavior of electronic devices. We demonstrate our approach to model heater cr yotrons, where the model is able to capture the stochastic switching dynamics ob served in the experiment. Our model shows 0.82% mean absolute erro r for switching probability."
University of TennesseeCyborgsEmergi ng TechnologiesMachine Learning
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