Apple Inc.

Youchang Kim

Christopher L Mills

Kenneth Waters

Embodiments relate to a neural processor that include a plurality of neural engine circuits and one or more planar engine circuits. The plurality of neural engine circuits can perform convolution operations of input data of the neural engine circuits with one or more kernels to generate outputs. The planar engine circuit is coupled to the plurality of neural engine circuits. The planar engine circuit generates an output from input data that corresponds to output of the neural engine circuits or a version of input data of the neural processor. The planar engine circuit can be configured to multiple modes. In a pooling mode, the planar engine circuit reduces a spatial size of a version of the input data. In an elementwise mode, the planar engine circuit performs an elementwise operation on the input data. In a reduction mode, the planar engine circuit reduces the rank of a tensor.

Multi-mode planar engine for neural processor

Embodiments relate to a neural processor that includes one or more neural engine circuits and planar engine circuits. The neural engine circuits can perform convolution operations of input data with one or more kernels to generate outputs. The planar engine circuit is coupled to the plurality of neural engine circuits. A planar engine circuit can be configured to multiple modes. In an elementwise mode, the planar engine circuit may combine two tensors by performing operations element by element. The planar engine circuit may support elementwise operation for two tensors that are in different sizes and ranks. The planar engine circuit may perform a broadcasting operation to duplicate one or more values across one or more channels to make a smaller tensor matching the size of the larger tensor.

Broadcasting mode of planar engine for neural processor

A neural processor includes one or more neural engine circuits and a planar engine circuit. The neural engine circuits can perform convolution operations of first input data with one or more kernels to generate a first output. The planar engine circuit receives second input data that corresponds to a version of the first input data. The planar engine circuit also receives third input data that includes fourth input data and fifth input data stored together in a dimension of third input data. The planar engine circuit performs a first elementwise operation between a version of the second input data and a version of the fourth input data to generate intermediate data. The planar engine circuit performs a second elementwise operation between the intermediate data and a version of the fifth input data to generate a second output.

Ternary mode of planar engine for neural processor

Embodiments relate to a neural processor that includes one or more neural engine circuits and planar engine circuits. The neural engine circuits can perform convolution operations of input data with one or more kernels to generate outputs. The planar engine circuit is coupled to the plurality of neural engine circuits. A planar engine circuit can be configured to multiple modes. In a reduction mode, the planar engine circuit may process values arranged in one or more dimensions of input to generate a reduced value. The reduced values across multiple input data may be accumulated. The planar engine circuit may program a filter circuit as a reduction tree to gradually reduce the data into a reduced value. The reduction operation reduces the size of one or more dimensions of a tensor.

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Cupertino, CA, United States of America

Youchang Kim