公开(公告)号：US20240111830A1

公开(公告)日：2024-04-04

申请号：US18534035

申请日：2023-12-08

Applicant: Intel Corporation

Inventor： Umer Iftikhar Cheema ,  Robert Simofi ,  Deepak Abraham Mathaikutty ,  Arnab Raha ,  Dinakar Kondru

IPC: G06F17/17 ,  G06F1/03

CPC classification number: G06F17/17 ,  G06F1/0307

Abstract: A non-linear activation function in a neural network may be approximated by one or more linear functions. The input range may be divided into input segments, each of which corresponds to a different exponent in the input range of the activation function and includes input data elements having the exponent. Target accuracies may be assigned to the identified exponents based on a statistics analysis of the input data elements. The target accuracy of an input segment will be used to determine one or more linear functions that approximate the activation function for the input segment. An error of an approximation of the activation function by a linear function for the input segment may be within the target accuracy. The parameters of the linear functions may be stored in a look-up table (LUT). During the execution of the DNN, the LUT may be used to execute the activation function.

公开(公告)号：US20240403616A1

公开(公告)日：2024-12-05

申请号：US18500229

申请日：2023-11-02

Applicant: Intel Corporation

Inventor： Umer Iftikhar Cheema ,  Kevin Brady ,  Robert Simofi ,  Colm O Faolain ,  Deepak Abraham Mathaikutty ,  Arnab Raha ,  Dinakar Kondru ,  Gary Baugh ,  Darren Crews ,  Fergal Connor

IPC: G06N3/048

Abstract: An activation function in a neural network may be approximated by one or more linear functions. A linear function may correspond to a segment of the input range of the activation function, e.g., a linear segment. A programmable look-up table may store slopes and intercepts of linear functions. A post processing engine (PPE) array executing the activation function may determine that an input data element of the activation function falls into the linear segment and compute an output of the linear function using the input data element. The output of the linear function may be used as the approximated output of the activation function. Alternatively, the PPE array may determine that the input data element is in a saturation segment and use a fixed value associated with the saturation segment as the approximated output of the activation function.

公开(公告)号：US20240013040A1

公开(公告)日：2024-01-11

申请号：US18474464

申请日：2023-09-26

Applicant: Intel Corporation

Inventor： Arnab Raha ,  Deepak Abraham Mathaikutty ,  Umer Iftikhar Cheema ,  Dinakar Kondru

IPC: G06N3/063 ,  G06N3/048 ,  G06N3/0464

CPC classification number: G06N3/063 ,  G06N3/048 ,  G06N3/0464

Abstract: A drain module may drain activations in an output tensor of a convolution from a PE array that performs the convolution. The drain module may extract activations generated in a collection of PE columns. The activations generated in the PE columns in the collection may be concatenated, e.g., activations generated in the first PE column of the collection may be followed by activations generated in the second PE column of the collection, and so on. The activations in the output tensor may be rearranged into activation vectors. Each activation vector may include activations in different output channels of the deep learning operation. The activations in each activation vector may have the same (X, Y) coordinate in the output tensor. The drain module may determine a memory address for an activation based on the activation's (X, Y, Z) coordinate in the output tensor and write the activation to the memory address.

公开(公告)号：US20240160695A1

公开(公告)日：2024-05-16

申请号：US18392618

申请日：2023-12-21

Applicant: Intel Corporation

Inventor： Dinakar Kondru ,  Deepak Abraham Mathaikutty ,  Arnab Raha ,  Umer Iftikhar Cheema

IPC: G06F17/17 ,  G06F1/035

CPC classification number: G06F17/17 ,  G06F1/0356

Abstract: A non-linear activation function may be approximated by linear functions. The input range of the activation function may be divided into input segments. One or more input segments may be selected based on statistical analysis of input data elements in the input range. A parameter of a first linear function that approximates the activation function for at least part of a selected input segment may be stored in a first portion of a first look-up table (LUT). The first portion of the first LUT is dedicated to a first group of post processing engines (PPEs). A parameter of a second linear function that approximates the activation function for at least part of an unselected input segment may be stored in a shared pool of LUT entries, which includes a second portion of the first LUT and a portion of a second LUT and is shared by multiple groups of PPEs.