US 2026/0086909 A1 · AUTOMATIC GENERATION OF COMPUTATION KERNELS FOR APPROXIMATING ELEMENTARY FUNCTIONS

Abstract

An apparatus for computing functions using polynomial-based approximation, comprising one or more processing circuitries configured for computing a polynomial-based approximant approximating a function by executing one or more iterations. Each iteration comprising computing the polynomial-based approximant using scaled fixed-point unit(s) according to a constructed set of coefficients, minimizing an approximation error of the computed polynomial-based approximant compared to the function while complying with one or more constraints selected from a group comprising at least: an accuracy, a compute graph size, a computation complexity, and a hardware utilization of the processing circuitry(s), adjusting one or more of the coefficients in case the approximation error is incompliant with the constraint(s) and initiating another iteration. The polynomial-based approximant and its adjusted set of coefficients for which the computed polynomial-based approximant complies with the constraint(s) may be output to one or more processing circuitries configured to approximate the function by computing the polynomial-based approximant.

Claims20

Dependent claims

A system for generating polynomial-based approximants, comprising:

at least one hardware processing circuitry comprising a plurality of logical hardware elements configured to realize at least one scaled fixed-point unit;
wherein the at least one hardware processing circuitry is configured for:
computing a first polynomial-based approximant approximating a function by configuring the plurality of logical hardware elements according to a first set of coefficients corresponding to a first target interval;
collecting a plurality of statistical values comprising data-statistic values indicative of the computing of the first polynomial-based approximant by the plurality of logical hardware elements;
analyzing the plurality of statistical values to identify a second target interval based on a distribution of input argument values observed in the data-statistic values, wherein the second target interval is narrower than the first target interval;
generating a second polynomial-based approximant optimized for the second target interval by constructing a second set of coefficients for approximating the function over the second target interval;
and
reconfiguring connections between the plurality of logical hardware elements to enable computation of the second polynomial-based approximant according to the second set of coefficients.

5 more elements

A method for generating polynomial-based approximants, comprising:

using at least one hardware processing circuitry comprising a plurality of logical hardware elements configured to realize at least one scaled fixed-point unit, wherein the method comprises:
computing a first polynomial-based approximant approximating a function by configuring the plurality of logical hardware elements according to a first set of coefficients corresponding to a first target interval;
collecting a plurality of statistical values comprising data-statistic values indicative of the computing of the first polynomial-based approximant by the plurality of logical hardware elements;
analyzing the plurality of statistical values to identify a second target interval based on a distribution of input argument values observed in the data-statistic values, wherein the second target interval is narrower than the first target interval;
generating a second polynomial-based approximant optimized for the second target interval by constructing a second set of coefficients for approximating the function over the second target interval;
and
reconfiguring connections between the plurality of logical hardware elements to enable computation of the second polynomial-based approximant according to the second set of coefficients.

4 more elements

Dependencies are inferred from the claim text.

Automatic generation of computation kernels for approximating elementary functions

Life of this patent

Automatic generation of computation kernels for approximating elementary functions

Life of this patent

Abstract

Claims20