Deep neural network architecture using piecewise linear approximation

A method for executing a machine learning model, the method comprising:

• computing, by first circuitry, a base-2 logarithm of an input of a floating-point operation in a machine learning model;
• computing, by second circuitry, an intermediate result by operating on the base-2 logarithm of the input;
• and
• applying, by third circuitry, a base-2 power function on the intermediate result to generate an output of the floating-point operation in the machine learning model.

One or more non-transitory computer-readable media storing instructions executable to perform operations for executing a machine learning model, the operations comprising:

• computing, by first circuitry, a base-2 logarithm of an input of a floating-point operation in a machine learning model;
• computing, by second circuitry, an intermediate result by operating on the base-2 logarithm of the input;
• and
• applying, by third circuitry, a base-2 power function on the intermediate result to generate an output of the floating-point operation in a machine learning model.