1. A method for computing a layer output for a convolutional neural network layer from a layer input for the convolutional neural network layer using a two-dimensional systolic array, the convolutional neural network layer having a plurality of kernels, each kernel having a respective matrix structure of weights, the method comprising: receiving a plurality of activation inputs, the plurality of activation inputs represented as a multi-dimensional matrix; forming a plurality of vector inputs from the plurality of activation inputs, each vector input comprising values from a distinct region within the multi-dimensional matrix; sending the plurality of vector inputs to one or more cells along a first dimension of the systolic array; generating a plurality of rotated kernel structures from each of the plurality of kernels, where generating a particular rotated kernel structure comprises shifting elements in the respective matrix structure for the kernel along one dimension; sending each kernel structure and each rotated kernel structure to one or more cells along a second dimension of the systolic array; causing the systolic array to generate an accumulated output based on the plurality of value inputs and the plurality of kernels; and generating the layer output from the accumulated output.