Abstract: Certain aspects of the present disclosure provide techniques and apparatus for efficient scaling of inputs to be processed by a machine learning model. An example method generally includes receiving, by a machine learning model, an input having a starting size in a plurality of dimensions. The method further includes scaling, by the machine learning model, the input in one or more dimensions of the plurality of dimensions to generate a scaled input, wherein the input is scaled in each respective dimension of the one or more dimensions based on a respective stride length determined based on a starting size in the respective dimension and a target size in the respective dimension, and the respective stride length associated with at least one dimension in the one or more dimensions comprises a non-integer value. The method further includes generating, by the machine learning model, an inference based on the scaled input.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for efficient scaling of inputs to be processed by a machine learning model. An example method generally includes receiving, by a machine learning model, an input having a starting size in a plurality of dimensions. The method further includes scaling, by the machine learning model, the input in one or more dimensions of the plurality of dimensions to generate a scaled input, wherein the input is scaled in each respective dimension of the one or more dimensions based on a respective stride length determined based on a starting size in the respective dimension and a target size in the respective dimension, and the respective stride length associated with at least one dimension in the one or more dimensions comprises a non-integer value. The method further includes generating, by the machine learning model, an inference based on the scaled input.

Abstract: Systems and techniques are provided for processing image data. A respective first value enclosed by a convolution kernel in each position of a plurality of positions of the convolution kernel along a row of the image data can be obtained and stored using a respective memory location associated with each position of the plurality of positions. Based on each respective first value, an accumulated value corresponding to a convolution output for each position of the plurality of positions can be updated. A plurality of second values enclosed by the convolution kernel in each position of the plurality of positions can be obtained. The plurality of second values includes a subset of the respective first values and an additional second value. A memory location used to store a first value not included in the plurality of second values can be updated to store the additional second value.