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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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首席:在具有异质FPGA的集群上部署异质模型的框架.

Yue Tang1, Yukai Song1, Naveena Elango2

  • 1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA.

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society
|December 20, 2024
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概括

CHEF有效地将复杂的多模式多任务深度神经网络 (MMMT DNNs) 映射到异构的FPGA集群中. 这种方法显著减少了先进AI硬件部署的延迟和搜索时间.

关键词:
不同质的FPGA集群.多模式多任务 (MMMT)

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科学领域:

  • 人工智能的人工智能
  • 计算机工程 计算机工程
  • 硬件加速器 硬件加速器

背景情况:

  • 深度神经网络 (DNN) 正在从单模单任务 (SMST) 演变为复杂的多模多任务 (MMMT) 架构.
  • 不同质的硬件系统,特别是使用FPGA的系统,越来越多地被采用来满足先进DNN的需求.
  • 目前用于将DNN映射到异质FPGA上的现有方法与MMMT模型的复杂性和规模相斗争.

研究的目的:

  • 开发一个有效的系统,在异质FPGA集群上实施MMMT模型.
  • 解决在现实的硬件环境中部署多种加速器和绘制复杂DNN的挑战.
  • 提出一个新的框架,优化硬件部署和加速器映射.

主要方法:

  • 引入了CHEF,一个框架,包括CHEF-A2F用于加速器到FPGA的部署和CHEF-M2A用于DNN到加速器的映射.
  • 对于硬件部署和加速器映射,CHEF-A2F采用了两阶段的协同优化方法.
  • CHEF-M2A旨在处理一般和实际的MMMT模型映射场景.

主要成果:

  • 展示了MMMT模型在真实异质FPGA集群上的首次实现.
  • 实现了MMMT模型执行的近最佳延迟.
  • 与详尽的搜索方法相比,搜索时间减少了1万倍.

结论:

  • CHEF为在异质FPGA上部署复杂的MMMT DNN提供了一种高效和实用的解决方案.
  • 该框架显著提升了大型AI模型硬件加速的最新技术.
  • 这项工作为专用硬件平台上的更复杂的人工智能应用铺平了道路.