Data transformation algorithms for tensor streaming processor

Abstract

Embodiments are directed to data transformation algorithms performed at a deterministic streaming processor. Blocks of input data is streamed from a memory of the processor via a superlane of a processor to a first functional slice of the processor. The first functional slice permutes each block of input data, and each permuted block is streamed back to the memory. Permuted blocks of input data are then streamed from the memory via the superlane to a second functional slice of the processor. The second functional slice aligns portions of each permuted block to lanes within the superlane. Aligned portions of permuted blocks are then streamed to a third functional slice of the processor. The third functional slice merges the aligned portions of the permuted blocks to generate result data in a transformation domain suitable for at least one convolutional layer of the ResNet-50 model.