论文标题
图形处理器上的捆绑包调整
Bundle Adjustment on a Graph Processor
论文作者
论文摘要
图形处理器（例如GraphCore的智能处理单元（IPU））是AI新型计算机架构的主要新浪潮的一部分，并且具有大量并行计算，分布在芯片上的内存和非常高的核心间通信带宽的一般设计，可以在任意图上进行消息传递算法的突破性性能。我们首次表明，使用高斯信念传播在图形处理器上可以非常快地解决捆绑调整的经典计算机视觉问题（BA）。我们的简单但完全平行的实现将单个IPU芯片上的1216个内核使用，例如，使用125个密钥帧和40毫秒以下的1919分解决了一个真正的BA问题，而CERES CPU库则解决了1450毫秒。进一步的代码优化肯定会在静态问题上增加这种差异，但是我们认为，图形处理的真正希望是灵活地优化代表空间AI问题的一般，动态变化的因子图。我们通过实验给出了这一点的迹象，该实验显示了GBP有效解决增量猛击问题的能力，并处理强大的成本功能和不同类型的因素。
Graph processors such as Graphcore's Intelligence Processing Unit (IPU) are part of the major new wave of novel computer architecture for AI, and have a general design with massively parallel computation, distributed on-chip memory and very high inter-core communication bandwidth which allows breakthrough performance for message passing algorithms on arbitrary graphs. We show for the first time that the classical computer vision problem of bundle adjustment (BA) can be solved extremely fast on a graph processor using Gaussian Belief Propagation. Our simple but fully parallel implementation uses the 1216 cores on a single IPU chip to, for instance, solve a real BA problem with 125 keyframes and 1919 points in under 40ms, compared to 1450ms for the Ceres CPU library. Further code optimisation will surely increase this difference on static problems, but we argue that the real promise of graph processing is for flexible in-place optimisation of general, dynamically changing factor graphs representing Spatial AI problems. We give indications of this with experiments showing the ability of GBP to efficiently solve incremental SLAM problems, and deal with robust cost functions and different types of factors.
