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REORDERING OF SPARSE DATA TO INDUCE SPATIAL LOCALITY FOR N-DIMENSIONAL SPARSE CONVOLUTIONAL NEURAL NETWORK PROCESSING
REORDERING OF SPARSE DATA TO INDUCE SPATIAL LOCALITY FOR N-DIMENSIONAL SPARSE CONVOLUTIONAL NEURAL NETWORK PROCESSING
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机译:重新排序稀疏数据,以诱导N维稀疏卷积神经网络处理的空间局部
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摘要
Exemplary embodiments maintain spatial locality of the data being processed by a sparse CNN. The spatial locality is maintained by reordering the data to preserve spatial locality. The reordering may be performed on data elements and on data for groups of co-located data elements referred to herein as "chunks". Thus, the data may be reordered into chunks, where each chunk contains data for spatially co-located data elements, and in addition, chunks may be organized so that spatially located chunks are together. The use of chunks helps to reduce the need to re-fetch data during processing. Chunk sizes may be chosen based on the memory constraints of the processing logic (e.g., cache sizes).
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