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Researchers from KAIST and DeepAuto AI have introduced InfiniteHiP, a novel long-context language model framework capable of processing up to 3 million tokens on a single GPU. This framework aims to enhance efficiency in token processing for large language models, allowing for extended context without the need for additional training. The proposal includes a three-stage pruning process to optimize the model's performance, making it a significant advancement in the field of artificial intelligence and deep learning.
``Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge,'' Sanggeon Yun, Ryozo Masukawa, Hanning Chen, SungHeon Jeong, Wenjun Huang, Arghavan Rezvani, Minhyoung Na, Yoshiki Yamaguchi, Mohsen Imani, https://t.co/w8llWQA4th
``SyncSpeech: Low-Latency and Efficient Dual-Stream Text-to-Speech based on Temporal Masked Transformer,'' Zhengyan Sheng, Zhihao Du, Shiliang Zhang, Zhijie Yan, Yexin Yang, Zhenhua Ling, https://t.co/Ic8UeI8ZMi
``NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing,'' Yifan Liang, Fangkun Liu, Andong Li, Xiaodong Li, Chengshi Zheng, https://t.co/xC0uKmFoPX
