Tensor Data Scattering and the Impossibility of Slicing Theorem
arXiv:2012.01982 · doi:10.1007/978-3-030-82193-7_18
Abstract
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.