ItNet：迭代神经网络，具有小图，用于准确，高效和随时语义分割
ItNet: iterative neural networks with small graphs for accurate, efficient and anytime semantic segmentation
摘要：
深度神经网络通常必须被压缩和加速，以便在低功耗（例如移动）设备中使用。最近，开发了通过利用存储器内计算以低功率提供高吞吐量和低延迟的并行硬件加速器。然而，为了利用这些优点，神经网络的计算图必须适合这些硬件系统的计算中存储器，该计算中存储器通常在大小上相当有限。在这项研究中，我们介绍了一类网络模型，有一个小的内存占用方面的计算图。为此，图被设计为通过迭代地执行单个网络构建块来包含循环。此外，这些所谓的迭代神经网络的准确性和延迟之间的权衡通过在训练和推理期间添加多个中间输出来改进。我们在CamVid和Cityscapes数据集上展示了最先进的语义分割结果，这些数据集在计算资源方面要求特别高。在消融研究中，研究了通过中间网络输出来改善网络训练以及在迭代过程中权重共享与网络大小之间的权衡。
英文摘要：
Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware accelerators were developed that offer high throughput and low latency at low power by utilizing in-memory computation. However, to exploit these benefits the computational graph of a neural network has to fit into the in-computation memory of these hardware systems that is usually rather limited in size. In this study, we introduce a class of network models that have a small memory footprint in terms of their computational graphs. To this end, the graph is designed to contain loops by iteratively executing a single network building block. Furthermore, the trade-off between accuracy and latency of these so-called iterative neural networks is improved by adding multiple intermediate outputs during both training and inference. We show state-of-the-art results for semantic segmentation on the CamVid and Cityscapes datasets that are especially demanding in terms of computational resources. In ablation studies, the improvement of network training by intermediate network outputs as well as the trade-off between weight sharing over iterations and the network size are investigated.
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。
