公开(公告)号：US20140143194A1

公开(公告)日：2014-05-22

申请号：US14070679

申请日：2013-11-04

Applicant: QUALCOMM Incorporated

Inventor： Young Cheul YOON ,  Ryan Michael CAREY ,  Roberto PADOVANI

IPC: G06N3/08

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/049 ,  G06N3/063 ,  G06N3/082 ,  G06N99/005

Abstract: Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.

公开(公告)号：US20140143190A1

公开(公告)日：2014-05-22

申请号：US14070652

申请日：2013-11-04

Applicant: QUALCOMM Incorporated

Inventor： Roberto PADOVANI ,  Young Cheul YOON ,  Naga BHUSHAN

IPC: G06N3/02

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/049 ,  G06N3/063 ,  G06N3/082 ,  G06N99/005

Abstract: Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.

Abstract translation: 基于线性神经元模型的人造神经系统中分段线性神经元建模和实现人造神经元的方法和装置。 用于操作人造神经元的一个示例性方法通常包括确定人造神经元的第一状态在第一区域内; 至少部分地基于第一组线性方程确定人造神经元的第二状态，其中所述第一组线性方程式至少部分地基于对应于所述第一区域的第一组参数; 确定人造神经元的第二状态在第二区域内; 以及至少部分地基于第二组线性方程确定所述人造神经元的第三状态，其中所述第二组线性方程式至少部分地基于对应于所述第二区域的第二组参数。

公开(公告)号：US20140143191A1

公开(公告)日：2014-05-22

申请号：US14070659

申请日：2013-11-04

Applicant: QUALCOMM Incorporated

Inventor： Roberto PADOVANI ,  Young Cheul YOON

IPC: G06N3/02

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/049 ,  G06N3/063 ,  G06N3/082 ,  G06N20/00

Abstract: Methods and apparatus for piecewise linear neuron modeling and implementing one or more artificial neurons in an artificial nervous system based on one or more linearized neuron models. One example method (for implementing a combination of a plurality of neuron models in a system of neural processing units) generally includes loading parameters for a first neuron model selected from the plurality of neuron models into a first neural processing unit, determining a first state of the first neural processing unit based at least in part on the parameters for the first neuron model, and determining a second state of the first neural processing unit based at least in part on the parameters for the first neuron model and on the first state. This method may also include updating the plurality of neuron models (e.g., by adding, deleting, or adjusting parameters for the first neuron model or another neuron model).

Abstract translation: 基于一个或多个线性化神经元模型的分段线性神经元建模和实现人造神经系统中的一个或多个人造神经元的方法和装置。 一个示例性方法（用于实现神经处理单元的系统中的多个神经元模型的组合）通常包括将从多个神经元模型中选择的第一神经元模型的参数加载到第一神经处理单元中，确定第一状态 所述第一神经处理单元至少部分地基于所述第一神经元模型的参数，以及至少部分地基于所述第一神经元模型的参数和所述第一状态来确定所述第一神经处理单元的第二状态。 该方法还可以包括更新多个神经元模型（例如，通过添加，删除或调整第一神经元模型或另一神经元模型的参数）。