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Tensor Data Scattering and the Impossibility of Slicing Theorem

Primary research

#1219

T1new

Canonical URL: http://arxiv.org/abs/2012.01982v3
Topic: unassigned (set during synthesis)
First seen: 2026-08-03 07:15:55
Last seen: 2026-08-03 07:15:55

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arXiv2026-08-03 07:15:23
Tensor Data Scattering and the Impossibility of Slicing Theorem
This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very important for performance analysis and accelerator optimization for implementing data scattering. The theorem shows how the impossibility of slicing happens in tensor data scattering. A sparsity measuring formula is provided, which can effectively indicate the storage efficiency of sparse tensor and the possibility of parallelly using it. A Python reference implementation is provided as ancillary material with this arXiv submission.