Sparse Architectures for Deep Learning: An Insightful Study

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Explore the innovative hardware architectures for deep learning developed by Joel Emer and Vivienne Sze at MIT. Discover how sparse architectures enhance efficiency and performance in processing and weights allocation.

  • Deep Learning
  • Sparse Architectures
  • Hardware
  • MIT
  • Innovations

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Presentation Transcript


  1. L17-1 6.5930/1 Hardware Architectures for Deep Learning Sparse Architectures Sparse Architectures Part 2 Part 2 April 8, 2024 Joel Emer and Vivienne Sze Massachusetts Institute of Technology Electrical Engineering & Computer Science

  2. L17-2 Output Stationary Sparse Weights April 8, 2024 Sze and Emer

  3. L17-3 Output Stationary Sparse Weights April 8, 2024 Sze and Emer

  4. L17-4 Parallel Weight Stationary - Sparse Weights April 8, 2024 Sze and Emer

  5. L17-5 Flattening April 8, 2024 Sze and Emer

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