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HARDWARE ARCHITECTURE AND PROCESSING UNITS FOR EXACT BAYESIAN INFERENCE WITH ON-LINE LEARNING AND METHODS FOR SAME
HARDWARE ARCHITECTURE AND PROCESSING UNITS FOR EXACT BAYESIAN INFERENCE WITH ON-LINE LEARNING AND METHODS FOR SAME
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机译：具有在线学习的精确贝叶斯推断的硬件体系结构和处理单元及其方法
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摘要
A reconfigurable computing architecture for Bayesian Online ChangePoint Detection (BOCPD) is provided. In an exemplary embodiment, the architecture may be employed for use in video processing, and more specifically to computing whether a pixel in a video sequence belongs to the background or to an object (foreground). Each pixel may be processed with only information from its intensity and its time history. The computing architecture employs unary fixed point representation for numbers in time, using pulse density or random pulse density modulation— i.e., a stream of zeros and ones, where the mean of that stream represents the encoded value.
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