Neural network processor

A method for neural network processing, the method comprises:

• applying, by a neural network processor, a group of neural network kernels of respective layers of a neural network to provide neural network output results;
• wherein the applying of the group of kernels comprises:
• applying a two dimensional (2D) kernel of the group of kernels on 2D layer input values of a first layer of the neural network to provide first layer output values, wherein the applying comprises scanning the 2D layer input values with the 2D kernel, wherein the scanning comprises flattening the 2D kernel, flattening the 2D layer input values, and storing first layer output values in a three dimensional (3D) memory unit, wherein the 3D memory unit comprises an array of shift registers, and wherein some of the shift registers are coupled to some other shift registers to shift data values across rows of the 3D memory unit;
• and
• applying a 3D kernel of the group of kernels, on 3D layer input values of a second layer of the neural network to provide second layer output values, wherein the applying comprises scanning at least one feature vector of the 3D layer input values with at least one vector of the 3D kernel;
• and
• outputting layer output values of a layer of the neural network from the array of shift registers, wherein the outputting includes performing a certain number of shift operations within at least one target shift register, and wherein the certain number is set based on a depth of next layer input values.

An integrated circuit comprising a three-dimensional (3D) memory unit, a processing circuit, a manipulator, and one or more memory units that are configured to cooperate in:

• applying a group of neural network kernels of respective layers of a neural network to provide neural network output results;
• wherein the applying of the group of kernels comprises:
• applying a two dimensional (2D) kernel of the group of kernels on 2D layer input values of a first layer of the neural network to provide first layer output values, wherein the applying comprising scanning the 2D layer input values with the 2D kernel, wherein the scanning comprises flattening the 2D kernel, flattening the 2D layer input values, and storing first layer output values in a three dimensional (3D) memory unit, and wherein the 3D memory unit comprises an array of shift registers, and wherein some of the shift registers are coupled to some other shift registers to shift data values across rows of the 3D memory unit;
• and
• applying a 3D kernel of the group of kernels, on 3D layer input values of a second layer of the neural network to provide second layer output values, wherein the applying comprises scanning at least one feature vector of the 3D layer input values with at least one vector of the 3D kernel;
• and
• outputting layer output values of a layer of the neural network from the array of shift registers, wherein the outputting includes performing a certain number of shift operations within at least one target shift register, and wherein the certain number is set based on a depth of next layer input values.

A non-transitory computer readable medium that stores instructions, which when executed by an integrated circuit, cause the integrated circuit to perform operations comprising:

• applying, by a neural network processor, a group of neural network kernels of respective layers of a neural network to provide neural network output results, wherein the applying of the group of kernels comprises:
• applying a two dimensional (2D) kernel of the group of kernels on 2D layer input values of a first layer of the neural network to provide first layer output values, wherein the applying comprising scanning the 2D layer input values with the 2D kernel, wherein the scanning comprises flattening the 2D kernel, flattening the 2D layer input values, and storing first layer output values in a three dimensional (3D) memory unit, wherein the 3D memory unit comprises an array of shift registers, and wherein some of the shift registers are coupled to some other shift registers to shift data values across rows of the 3D memory unit;
• and
• applying a 3D kernel of the group of kernels, on 3D layer input values of a second layer of the neural network to provide second layer output values, wherein the applying comprises scanning at least one feature vector of the 3D layer input values with at least one vector of the 3D kernel;
• and
• outputting layer output values of a layer of the neural network from the array of shift registers, wherein the outputting includes performing a certain number of shift operations within at least one target shift register, and wherein the certain number is set based on a depth of next layer input values.