Abstract: Aspects described herein provide a method of processing data in a machine learning model, including: receiving first domain input data; transforming the first domain input data to second domain input data via a domain transformation function; providing the second domain input data to a first layer of a machine learning model; processing the second domain input data in the first layer of the machine learning model according to a set of layer weights; and outputting second domain output data from the first layer of the machine learning model.

Abstract: A method for an artificial neural network includes receiving an input. A quantization threshold is determined based on the input, or a characteristic or type of the input. Neural network values, such as weights or activations, of one or more layers of the artificial neural network are quantized according to the quantization threshold. The artificial neural network generates an output based on the quantized neural network values.

Abstract: Certain aspects of the present disclosure provide techniques for generating execution schedules, comprising receiving a data flow graph for a process, where data flow graph comprises a plurality of nodes and a plurality of edge; generating a topological ordering for the data flow graph based at least in part on memory utilization of the process; generating a first modified topological ordering by inserting, into the topological ordering, one or more new nodes corresponding to memory access based on a predefined memory capacity; allocating units of memory in the memory based on the first modified topological ordering; and generating a second modified topological ordering by rearranging one or more nodes in the first modified topological ordering, where the second modified topological ordering enables increased parallel utilization of a plurality of hardware components.

Abstract: Aspects described herein provide a method of processing data in a machine learning model, including: receiving first domain input data; transforming the first domain input data to second domain input data via a domain transformation function; providing the second domain input data to a first layer of a machine learning model; processing the second domain input data in the first layer of the machine learning model according to a set of layer weights; and outputting second domain output data from the first layer of the machine learning model.