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Gaussian Mixture Models (GMMs) are powerful tools for probability density modeling and soft clustering. They are widely used in data mining, signal processing and computer vision. In many applications, we need to estimate the parameters of a GMM from data before working with it. This task can be handled by the Expectation-Maximization algorithm for Gaussian Mixture Models (EM-GMM), which is computationally...
Coarse-grained reconfigurable arrays (CGRAs) architectures aim to offer high performance at low power consumption, especially for digital signal processing and streaming applications. To fully exploit the computing capability of CGRA, it is essential to develop a scheduling algorithm which maps operations over processing elements in CGRA. Modulo scheduling [1] is known as the state-of-art algorithm...
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