Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volumetric (3D) data acquired by CT, MRI and Microscopy acquisition methods. To deploy convolutional nets in practical working systems, it is important to solve the efficient inference problem. Namely, one should be able to apply an already-trained convolutional network to many large images using limited computational resources. In this paper we present PZnet, a CPU-only engine that can be used to perform inference for a variety of 3D convolutional net architectures. PZNet outperforms MKL-based CPU implementations of PyTorch and Tensorflow by more than 3.5x for the popular U-net architecture. Moreover, for 3D convolutions with low featuremap numbers, cloud CPU inference with PZnet outperfroms cloud GPU inference in terms of cost efficiency.
翻译:生物医学分析领域的许多任务都涉及分析CT、MRI和显微镜采集方法获得的体积(3D)数据。在实际操作系统中部署进化网十分重要。也就是说,人们应该能够利用有限的计算资源对许多大型图像应用已经受过训练的进化网。在本文中,我们展示了PZnet,这是一个CPU专用的引擎,可用于为各种3D进化网结构进行推断。PZNet在成本效率方面优于基于MKL的CPU在PyToirch和Tensorflows等通用Unet结构中采用3.5x以上的MKL CPU。此外,对于具有低地貌数字的3D进化器,用PZnet从云式GPU的推断中推断出云式的云式CPU。