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  • pdf文档 TVM Meetup: Quantization

    Calculations are different from FP32 Conv2D https://discuss.tvm.ai/t/tf-lite-quantized-conv2d-operator-conversion/2651/8 𝑟𝑒𝑎𝑙_𝑣𝑎𝑙𝑢𝑒 = 𝒔𝒄𝒂𝒍𝒆 ∗ (𝑞𝑢𝑎𝑛𝑡𝑖𝑧𝑒𝑑_𝑣𝑎𝑙𝑢𝑒 − 𝒛𝒆𝒓𝒐_𝒑𝒐𝒊𝒏𝒕)© Pre-quantized model support. Contributions are welcomed. • We need new/tuned TVM schedules using fast Integer operations like Intel VNNI, ARM Dot, Nvidia DP4A • Full pipeline is available. Please try
    0 码力 | 19 页 | 489.50 KB | 6 月前
    3
  • pdf文档 Trends Artificial Intelligence

    Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes (excl. China & USA) USA 2014 20236 …Charts Paint Thousands of Words AI & Physical World Ramps = Fast + Data-Driven 6 A Ride Share vs. Autonomous Taxi Provider, San Francisco Operating Zone Market Share mission (2004) was ‘to give people the power to share and make the world more open and connected.’ Fast forward to today with the world’s organized, connected and accessible information being supercharged
    0 码力 | 340 页 | 12.14 MB | 5 月前
    3
  • pdf文档 清华大学 DeepSeek+DeepResearch 让科研像聊天一样简单

    applied at a constant loading rate of 10 mm-min until the real-time force curve on the monitor screen fast drop indicating failure occurred. ln addition, the left valve of each mussel was examined for compressive applied at a constant loading rate of 10 mm/min until the real-time force curve on the monitor screen fast drop indicating failure occurred. 改写降重指令 指令:我想让你充当科研写作专家,并提供一些英文或中文段落,你的任务是用原文改写段落。你应该使用 人工智能
    0 码力 | 85 页 | 8.31 MB | 8 月前
    3
  • pdf文档 DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

    careful engineering optimization to manage the GPU memory and RAM pressure, and meanwhile maintain a fast training speed. For this goal, we implement the following engineering optimizations. (1) Firstly, mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024. N. Shazeer. Fast transformer decoding: One write-head is all you need. CoRR, abs/1911.02150, 2019. URL http://arxiv
    0 码力 | 52 页 | 1.23 MB | 1 年前
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