Qualcomm Incorporated

Edward Liao

Vikram Makam Gupta

Jeffrey Alexander Levin

Victor Hokkiu Chan

Dexu Lin

Somdeb Majumdar

Michael Campos

Aaron Douglass Lamb

Yinyin Liu

Karamvir Chatha

Sarah Paige Gibson

Erik Christopher Malone

Computing a non-linear function &#x192;(x) in hardware or embedded systems can be complex and resource intensive. In one or more aspects of the disclosure, a method, a computer-readable medium, and an apparatus are provided for computing a non-linear function &#x192;(x) accurately and efficiently in hardware using look-up tables (LUTs) and interpolation or extrapolation. The apparatus may be a processor. The processor computes a non-linear function &#x192;(x) for an input variable x, where &#x192;(x)=g(y(x),z(x)). The processor determines an integer n by determining a position of a most significant bit (MSB) of an input variable x. In addition, the processor determines a value for y(x) based on a first look-up table and the determined integer n. Also, the processor determines a value for z(x) based on n and the input variable x, and based on a second look-up table. Further, the processor computes &#x192;(x) based on the determined values for y(x) and z(x).

Approximation of non-linear functions in fixed point using look-up tables

Methods and apparatus are provided for effecting modulation using global scalar values in a spiking neural network. One example method for operating an artificial nervous system generally includes determining one or more updated values for artificial neuromodulators to be used by a plurality of entities in a neuron model and providing the updated values to the plurality of entities.

Effecting modulation by global scalar values in a spiking neural network

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San Diego, CA, United States of America

Edward Liao