Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It has been widely recognized that the dynamic range information of an application can be exploited to reduce the datapath bitwidth of either processors or ASICs, and therefore the overall circuit area, delay, and por consumption. Recent advances in analytical dynamic range estimation methods indicate that by systematically decomposing the system inputs into orthonormal random variables using a mathematical procedure called polynomial chaos expansion (PCE), output statistics of interest can be obtained for both linear and nonlinear systems. Despite its por for capturing both spatial and temporal correlation, the application of this method has been limited only to near-Gaussian inputs. ,  propose the first algorithm with the capacity of handling both near-Gaussian and non-Gaussian input signals. Our method is based on the use of independent component analysis (ICA). Our experiments show that the new algorithm can reduce the original relative errors of 2nd order moments from 25% - 65% to 1% - 2%.
['Bin Wu', 'Jianwen Zhu', 'Farid N. Najm']
I am writing a research paper on a idea A non-parametric approach for dynamic range estimation of nonlinear systems
[{"from": "human", "value": "I am writing a research paper on a idea A non-parametric approach for dynamic range estimation of nonlinear systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It has been widely recognized that the dynamic range information of an application can be exploited to reduce the datapath bitwidth of either processors or ASICs, and therefore the overall circuit area, delay, and por consumption. Recent advances in analytical dynamic range estimation methods indicate that by systematically decomposing the system inputs into orthonormal random variables using a mathematical procedure called polynomial chaos expansion (PCE), output statistics of interest can be obtained for both linear and nonlinear systems. Despite its por for capturing both spatial and temporal correlation, the application of this method has been limited only to near-Gaussian inputs. ,  propose the first algorithm with the capacity of handling both near-Gaussian and non-Gaussian input signals. Our method is based on the use of independent component analysis (ICA). Our experiments show that the new algorithm can reduce the original relative errors of 2nd order moments from 25% - 65% to 1% - 2%."}]