Depth concatenation using a matrix computation unit

A system comprising one or more computers and one or more storage devices storing first instructions that when executed by the one or more computers cause the one or more computers to perform first operations 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 x1 by y1 by z1 and an input tensor having dimensions x1 by y1 by z2 along a depth dimension to generate an output tensor having dimensions x1 by y1 by (z1+z2);
• 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 second 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:
• obtaining a first input depth vector for the spatial location in the first input tensor and a second input depth vector for the spatial location in the second input tensor, wherein each of the first input depth vector and the second input depth vector are made up of one or more respective fixed size chunk vectors;
• identifying a first chunk vector from among the chunk vectors in the first input depth vector and the second input depth vector, wherein the first chunk vector is a first chunk vector according to an ordering of the chunk vectors to include one or more non-padded entries and one or more padded entries;
• multiplying, using the matrix computation unit, a second chunk vector that follows the first chunk vector in the ordering by a first shift weight matrix for the depth concatenation layer to generate a shifted second chunk vector that has zeroes as its first (max−n) entries and a first n entries of the second chunk vector as its remaining entries, wherein n is the number of padded entries in the first chunk vector and max is the fixed size of the chunk vectors;
• and
• adding, using the matrix computation unit, the shifted second chunk vector and a modified first chunk vector to generate a concatenated chunk vector that is part of the layer output, the modified first chunk vector having the non-padded entries of the first chunk vector and zeroes for the remaining entries of the modified first chunk vector,
• wherein the first shift weight matrix for the depth concatenation layer is a max by max matrix.

One or more non-transitory computer storage media encoded with first instructions that when executed by one or more computers cause the one or more computers to perform first operations 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 x1 by y1 by z1 and an input tensor having dimensions x1 by y1 by z2 along a depth dimension to generate an output tensor having dimensions x1 by y1 by (z1+z2);
• 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 second 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:
• obtaining a first input depth vector for the spatial location in the first input tensor and a second input depth vector for the spatial location in the second input tensor, wherein each of the first input depth vector and the second input depth vector are made up of one or more respective fixed size chunk vectors;
• identifying a first chunk vector from among the chunk vectors in the first input depth vector and the second input depth vector, wherein the first chunk vector is a first chunk vector according to an ordering of the chunk vectors to include one or more non-padded entries and one or more padded entries;
• multiplying, using the matrix computation unit, a second chunk vector that follows the first chunk vector in the ordering by a first shift weight matrix for the depth concatenation layer to generate a shifted second chunk vector that has zeroes as its first (max−n) entries and a first n entries of the second chunk vector as its remaining entries, wherein n is the number of padded entries in the first chunk vector and max is the fixed size of the chunk vectors;
• and
• adding, using the matrix computation unit, the shifted second chunk vector and a modified first chunk vector to generate a concatenated chunk vector that is part of the layer output, the modified first chunk vector having the non-padded entries of the first chunk vector and zeroes for the remaining entries of the modified first chunk vector,
• wherein the first shift weight matrix for the depth concatenation layer is a max by max matrix.