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arXiv · 2009.08685

GrateTile: Efficient Sparse Tensor Tiling for CNN Processing

Yu-Sheng Lin·Hung Chang Lu·Yang-Bin Tsao·Yi-Min Chih·Wei-Chao Chen·Shao-Yi Chien
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Abstract

We propose GrateTile, an efficient, hardwarefriendly data storage scheme for sparse CNN feature maps (activations). It divides data into uneven-sized subtensors and, with small indexing overhead, stores them in a compressed yet randomly accessible format. This design enables modern CNN accelerators to fetch and decompressed sub-tensors on-the-fly in a tiled processing manner. GrateTile is suitable for architectures that favor aligned, coalesced data access, and only requires minimal changes to the overall architectural design. We simulate GrateTile with state-of-the-art CNNs and show an average of 55% DRAM bandwidth reduction while using only 0.6% of feature map size for indexing storage.

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Yu-Sheng Lin, Hung Chang Lu, Yang-Bin Tsao, Yi-Min Chih, Wei-Chao Chen, Shao-Yi Chien. 2020-09-18. GrateTile: Efficient Sparse Tensor Tiling for CNN Processing. https://arxiv.org/abs/2009.08685

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