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Branchpoint Prediction Using Self-Attention Based Deep Neural Networks
http://doi.org/10.5626/JOK.2020.47.4.343
Splicing is a ribonucleic acid (RNA) process of creating a messenger RNA (mRNA) translated into proteins. Branchpoints are sequence elements of RNAs essential in splicing. This paper proposes a novel method for branchpoint prediction. Identification of branchpoints involves several challenges. Branchpoint sites are known to depend on several sequence patterns, called motifs. Also, a branchpoint distribution is highly biased, imposing a class-imbalanced problem. Existing approaches are limited in that they either rely on handcrafted sequential features or ignore the class imbalance. To address those difficulties, the proposed method incorporates 1) Attention mechanisms to learn sequence-positional long-term dependencies, and 2) Regularization with triplet loss to alleviate the class imbalance. Our method is comparable to the state-of-the-art performance while providing rich interpretability on its decisions.
Task-to-Tile Binding Technique for NoC-based Manycore Platform with Multiple Memory Tiles
Jintaek Kang, Taeyoung Kim, Sungchan Kim, Soonhoi Ha
The contention overhead on the same channel in an NoC architecture can significantly increase a communication delay due to the simultaneous communication requests that occur. To reduce the overall overhead, we propose task-to-tile binding techniques for an NoC-based manycore platform, whereby it is assumed that the task mapping decision has already made. Since the NoC architecture may have multiple memory tiles as its size grows, memory clustering is used to balance the load of memory by making applications access different memory tiles. We assume that the information on the communication overhead of each application is known since it is specified in a dataflow task graph. Using this information, this paper proposes two heurisitics that perform binding of multiple tasks at once based on a proper memory clustering method. Experiments with an NoC simulator prove that the proposed heurisitic shows performance gains that are 25% greater than that of the previous binding heuristic.
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