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    • The ALICE trigger electronics

    M Krivda,D Evans,G Jones,L Sandor,P Jovanovic,O Villalobos Baillie,I Kralik,D Tapia Takaki,A Jusko,C Lazzeroni,A Bhasin, Hal S Scott,J Urban,R Lietava

    mag（2007）

    Cited 2|Views8
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
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    【要点】：本文介绍了ALICE实验中触发电子学系统的设计、实现与性能，创新点在于其高效率的数据采集与低延迟的触发机制。
    

    【方法】：作者通过详细设计触发电子学系统的各个组件，包括数据采集、触发逻辑以及数据传输等，实现了高效能的触发过程。
    

    【实验】：在ALICE实验中，使用特定数据集对触发电子学系统进行了测试，实验结果表明该系统能够在保证低延迟的同时，有效处理大量数据。