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An FPGA multiprocessor architecture for Bayesian online change point detection using stochastic computation

机译:使用随机计算的贝叶斯在线改变点检测FPGA多处理器架构

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In this paper we report on an event-based stochastic architecture for the Adams/McKay Bayesian Online Change Point Detection algorithm (BOCPD) [1]. In the stochastic computational structures, probabilities are represented natively as stochastic events and computation is carried out directly with these probabilities and not probability density functions. A fully programmable BOCPD processor is synthesized in VHDL. The BOCPD algorithm with on-line learning, to perform foreground/background image segmentation with online learning. Running on a single Kintex 7 FPGA (Opal Kelly XEM7350-K410T) the architecture is capable of real-time processing a 160 x 120 pixels image, at 10 frames per second. (C) 2020 Elsevier B.V. All rights reserved.
机译:在本文中,我们报告了ADAMS / McKay贝叶斯在线改变点检测算法(Bocpd)的事件的随机架构。在随机计算结构中,概率本地表示,因为随机事件和计算直接与这些概率直接执行,而不是概率密度函数。在VHDL中合成完全可编程的BOCPD处理器。具有在线学习的Bocpd算法,以在线学习执行前景/背景图像分割。在单个KINTEX 7 FPGA(OPAL KELLY XEM7350-K410T)上运行该架构能够实时处理160 x 120像素图像,每秒10帧。 (c)2020 Elsevier B.v.保留所有权利。

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