基于分层时间记忆空间分布的OpenCL-accelerated视频流对象分类
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
摘要：
我们提出了一种方法来分类视频流中的对象，使用大脑启发的层次时间记忆（HTM）算法。对象分类是一项具有挑战性的任务，由于其独特的能力，人类仍然显著优于机器学习算法。我们已经实现了一个系统，实现了非常有前途的性能方面的识别精度。不幸的是，进行更高级的实验对计算要求非常高;在标准CPU上运行的一些试验可能需要长达几天的时间才能获得960 x540视频流帧。因此，我们决定使用OpenCL加速系统的选定部分。特别是，我们试图确定在何种程度上移植选定的和计算要求的核心部分可能会加快计算。通过一系列的实验，该系统的分类精度进行了检查，并给出了性能的F1分数作为列，突触，$min\_overlap$和$winners\_set\_size$的数量的函数。该系统分别对$min\_overlap=4$和256个突触实现了0.95和0.91的最高F1分数。我们还使用不同的硬件设置进行了一系列实验，并测量了CPU/GPU加速。对于256个突触和1024列，最佳内核加速达到632倍和207倍。然而，包括传输时间在内的整体加速明显较低，对于相同的设置，达到6.5倍和3.2倍。
英文摘要：
We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where humans still significantly outperform machine learning algorithms due to their unique capabilities. We have implemented a system which achieves very promising performance in terms of recognition accuracy. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore we have decided to accelerate selected parts of the system using OpenCL. In particular, we seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and 256 synapses, respectively. We have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.
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