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.

本文研究了基于 Graphcore 智能处理单元（IPU）的图处理器，证明了用高斯置信传播算法可以极快地解决经典计算机视觉问题中的束调整问题，并表明图处理的真正优势在于处理表示空间 AI 问题的一般动态因子图的灵活本地优化。

基于图处理器的束调整

Bundle Adjustment on a Graph Processor

We discuss and predict the evolution of Simultaneous Localisation and Mapping
(SLAM) into a general geometric and semantic `Spatial AI' perception capability
for intelligent embodied devices. A big gap remains between the visual
perception performance that devices such as augmented reality eyewear or
comsumer robots will require and what is possible within the constraints
imposed by real products. Co-design of algorithms, processors and sensors will
be needed. We explore the computational structure of current and future Spatial
AI algorithms and consider this within the landscape of ongoing hardware
developments.

本文讨论和预测了 Simultaneous Localisation and Mapping（SLAM）进化为智能设备的通用几何和语义 “空间 AI” 感知能力，探索了当前和未来空间 AI 算法的计算结构，并考虑其与硬件发展的关系。