Depth concatenation using a matrix computation unit

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for depth concatenation using a matrix computation unit. One of the methods includes: receiving a request to process network inputs to a neural network using an integrated circuit, the neural network comprising a depth concatenation neural network layer; and generating instructions that, when executed by the integrated circuit, cause the integrated circuit to perform operations comprising: for each spatial location in a first input tensor to the depth concatenation layer and a second input tensor to the depth concatenation layer: multiplying, using the matrix computation unit, a second depth vector for the spatial location by a shift weight matrix for the depth concatenation layer to generate a shifted second depth vector; and adding the shifted second depth vector and a first input depth vector for the spatial location to generate a concatenated depth vector.

What is claimed is: 
     
         1 . A method comprising:
 receiving a request to process network inputs to a neural network using an integrated circuit that performs neural network computations in hardware using a matrix computation unit, the neural network comprising a depth concatenation neural network layer that specifies a concatenation of an input tensor having dimensions x 1  by y 1  by z 1  and an input tensor having dimensions x 1  by y 1  by z 2  along a depth dimension to generate an output tensor having dimensions x 1  by y 1  by (z 1 +z 2 ); and   generating instructions that, when executed by the integrated circuit, cause the integrated circuit to, during processing of a network input by the neural network, generate a layer output tensor that satisfies the specification of the depth concatenation neural network layer by performing operations comprising:
 for each spatial location in a first input tensor to the depth concatenation layer and a second input tensor to the depth concatenation layer:
 multiplying, using the matrix computation unit, a second depth vector for the spatial location in the second input tensor by a shift weight matrix for the depth concatenation layer to generate a shifted second depth vector that has zeroes as the first z 1  entries and entries of the second depth vector as the last z 2  entries; and 
 adding the shifted second depth vector and a first input depth vector for the spatial location in the first input tensor to generate a concatenated depth vector, the first input depth vector having entries of the first input depth vector as the first z 1  entries of the first input depth vector and zeroes as the last z 2  entries of the first input depth vector.