Road extraction for the high-resolution remote sensing images has important applications in urban planning, traffic management, geographic information systems(GIS) and other fields. However, The impact of the environment such as pedestrians and farms causes discontinuity and incompleteness in extraction results. In this paper, We present D-Linknet with transformer and Concurrent Spatial and Channel Squeeze and Channel Excitation(scSE) module(TSDlinknet) which is built with D-Linknet architecture. Firstly, we use transformer encoders in the central part instead of D-Block to handle long-range dependencies efficiently. Secondly, scSE module is added after each convolutional layer in the decoding stage to refine spatial information and channel information of the feature map. Experimental results on CHN6-CUG road dataset demonstrate that our method outperforms D-Linknet101 by 5.6% IOU higher, in addition, our extraction results are more continuous and complete.

Timing errors are a growing concern for system resilience as technology continues to scale. It is problematic to use low-fidelity errors such as single-bit flips to model realistic timing errors. We address the lack of holistic methodology and tool for evaluating resilience of applications against timing errors. The proposed technique is able to rapidly inject high-fidelity and configurable timing errors to applications at the instruction level. Our implementation has no runtime dependencies on proprietary tools, enabling full parallelism of error injection campaign. Furthermore, because an injection point may not generate an actual error for a particular application run, we propose an acceleration technique to maximize the likelihood of generating errors that contribute to the overall campaign with speedup up to 7X. With our tool, we show that realistic timing errors lead to distinct error profiles from those of radiation-induced errors at both the instruction level and the application level.

Quality control plays a key role in approximate computing to save the energy and guarantee that the quality of the computation outcome satisfies users' requirement. Previous works proposed a hybrid architecture, composed of a classifier for error prediction and an approximate accelerator for approximate computing using well trained neural-networks. Only inputs predicted to meet the quality are executed by the accelerator. However, the design of this hybrid architecture, relying on one-pass training process, has not been fully explored. In this paper, we propose a novel optimization framework. It advocates an iteratively training process to coordinate the training of the classifier and the accelerator with a judicious selection of training data. It integrates a dynamic threshold tuning algorithm to maximize the invocation of the accelerator (i.e., energy-efficiency) under the quality requirement. At last, we propose an efficient algorithm to explore the topologies of the accelerator and the classifier comprehensively. Experimental results shows significant improvement on the quality and the energy-efficiency compared to the conventional one-pass training method.