ItNet: iterative neural networks with small graphs for accurate, efficient and anytime semantic segmentation
This addresses the challenge of efficient neural network execution on mobile and hardware-accelerated devices, offering a domain-specific solution for semantic segmentation.
The paper tackles the problem of deploying deep neural networks on low-power devices with limited memory by introducing iterative neural networks (ItNet) that use a small computational graph with loops and intermediate outputs. It achieves state-of-the-art results for semantic segmentation on CamVid and Cityscapes datasets, improving the accuracy-latency trade-off.
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.