基于随机数字信号的快速节能贝叶斯视差计算
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
摘要：
利用双目视差信息重建视觉场景的三维几何结构是计算机视觉和移动机器人领域的一个重要问题，它可以用贝叶斯推理问题来描述。然而，使用高级贝叶斯模型计算全视差分布通常是一个棘手的问题，并且即使使用简单的模型也证明在计算上具有挑战性。在本文中，我们将展示如何使用分布式内存和交替表示的数据作为随机比特流的概率硬件可以解决这个问题，具有高性能和能源效率。我们提出了一种方法来表示离散概率分布使用随机数据表示和贝叶斯融合使用这些表示，并显示该方法可以应用于偶校验计算。我们评估系统使用模拟随机实现，并讨论可能的硬件实现这种架构和它们的潜力，感觉运动处理和机器人。
英文摘要：
Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
