Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. However, the automatically constructed datasets comprise amounts of low-quality sentences and noisy words, and the current Distantly Supervised methods ignore these noisy data, resulting in unacceptable accuracy. To mitigate this problem, we present a novel Distantly Supervised approach SEGRE (Semantic Enhanced Graph attention networks Relation Extraction) for improved relation extraction. Our model first uses word position and entity type information to provide abundant local features and background knowledge. Then it builds the dependency trees to remove noisy words that are irrelevant to relations and employs Graph Attention Networks (GATs) to encode syntactic information, which also captures the important semantic features of relational words in each instance. Furthermore, to make our model more robust against noisy words, the intra-bag attention module is used to weight the bag representation and mitigate noise in the bag. Through extensive experiments on Riedel New York Times (NYT) and Google IISc Distantly Supervised (GIDS) datasets, we demonstrate SEGRE’s effectiveness.

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Large scale synchronous network-on-chip (NoC) requires complex clock tree design, which leads to a large area overhead and power consumption. Based on handshaking protocols, asynchronous NoC does not have global clock tree distribution, which results in a natural power saving mode without any explicit clock gating. However, the faults occurring in such asynchronous networks will seriously affect their performances. In this paper, we propose AFTER, an Asynchronous Fault-TolErant Router, which uses the quasi delay insensitive (QDI) logic. The proposed router is able to detect the faults of ports and links. Then, a fault-tolerant routing mechanism, based on the port priority in different quadrants, is proposed to maximize the number of packets that can be transmitted via the shortest paths. In this way, the fault-tolerance of asynchronous routers can be achieved. Besides that, AFTER could also achieve high scalability, and is suitable for the large scale globally asynchronous locally synchronous (GALS) system. The experimental results show that, when faults occur in the network, AFTER has a better fault-tolerance performance than the reference.