
Sparse Architectures for Deep Learning: An Insightful Study
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.
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Presentation Transcript
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
L17-2 Output Stationary Sparse Weights April 8, 2024 Sze and Emer
L17-3 Output Stationary Sparse Weights April 8, 2024 Sze and Emer
L17-4 Parallel Weight Stationary - Sparse Weights April 8, 2024 Sze and Emer
L17-5 Flattening April 8, 2024 Sze and Emer