REORDERING OF SPARSE DATA TO INDUCE SPATIAL LOCALITY FOR N-DIMENSIONAL SPARSE CONVOLUTIONAL NEURAL NETWORK PROCESSING

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...

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Bibliographic Details
Main Authors Subramoney, Sreenivas, Omer, Om, Thyagharajan, Anirud, Laddha, Prashant
Format Patent
LanguageEnglish
French
German
Published 03.11.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary: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).
Bibliography:Application Number: EP20200209681