Leonid Barenboim
ABSTRACT
The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.
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Digital Library
- J. Zhang, S. H. Yeung, Y. Shu, B. He and W. Wang, "Efficient memory management for gpu-based deep learning systems," arXiv:1903.06631, 2019.Google Scholar
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- Y. Pisarchyk and J. Lee, "Efficient Memory Management for Deep Neural Net Inference," arXiv preprint arXiv:2001.03288, 2020.Google Scholar
- L. Li, H. Feng and J. Xue, "Compiler-directed Scratchpad Memory Management via Graph Coloring," ACM Trans. Archit. Code Optim., vol. 6, no. 3, pp. 1-17, 2009.Google ScholarDigital Library
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Index Terms
- Memory Allocation for Neural Networks using Graph Coloring
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