Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation

Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation

Efficient implementation of the activation function is an important part in the hardware design of artificial neural network. Sigmoid function is one of the most widely used activation function. In this paper, an efficient architecture for digital hardware implementation of sigmoid function is prese...

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Main Authors: Abdullah, Embong, Rohani, Abu Bakar, Syahrulanuar, Ngah
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/3663/
http://umpir.ump.edu.my/id/eprint/3663/1/44ICoCSIM.pdf
id ump-3663
recordtype eprints
spelling ump-36632018-05-02T06:54:38Z http://umpir.ump.edu.my/id/eprint/3663/ Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation Abdullah, Embong Rohani, Abu Bakar Syahrulanuar, Ngah QA76 Computer software Efficient implementation of the activation function is an important part in the hardware design of artificial neural network. Sigmoid function is one of the most widely used activation function. In this paper, an efficient architecture for digital hardware implementation of sigmoid function is presented. The proposed method used second order nonlinear function (SONF) as a foundation and further improves the result by using 320 bits of read only memory (ROM) for storing a differential lookup table (differential LUT). The method proves to be more effective considering the smallest deviation of sigmoid function achieved in comparison to conventional LUT and SONF. Employing this method for hardware-based ANN in the indoor positioning system have shown that, ANN can detect the target position almost as accurate as software implementation with a speed 13 times faster. Thus the proposed idea is suitable to be implemented in a hardware-based ANN for various real-time applications. 2012-12-03 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3663/1/44ICoCSIM.pdf Abdullah, Embong and Rohani, Abu Bakar and Syahrulanuar, Ngah (2012) Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation. In: International Conference on Computational Science and Information Management (ICoCSIM), 3-5 December 2012 , Toba Lake, North Sumatera, Indonesia. pp. 223-226..
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Abdullah, Embong
Rohani, Abu Bakar
Syahrulanuar, Ngah
Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
description Efficient implementation of the activation function is an important part in the hardware design of artificial neural network. Sigmoid function is one of the most widely used activation function. In this paper, an efficient architecture for digital hardware implementation of sigmoid function is presented. The proposed method used second order nonlinear function (SONF) as a foundation and further improves the result by using 320 bits of read only memory (ROM) for storing a differential lookup table (differential LUT). The method proves to be more effective considering the smallest deviation of sigmoid function achieved in comparison to conventional LUT and SONF. Employing this method for hardware-based ANN in the indoor positioning system have shown that, ANN can detect the target position almost as accurate as software implementation with a speed 13 times faster. Thus the proposed idea is suitable to be implemented in a hardware-based ANN for various real-time applications.
format Conference or Workshop Item
author Abdullah, Embong
Rohani, Abu Bakar
Syahrulanuar, Ngah
author_facet Abdullah, Embong
Rohani, Abu Bakar
Syahrulanuar, Ngah
author_sort Abdullah, Embong
title Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
title_short Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
title_full Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
title_fullStr Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
title_full_unstemmed Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation
title_sort local position estimation using an artificial neural network based model with a hardware implementation
publishDate 2012
url http://umpir.ump.edu.my/id/eprint/3663/
http://umpir.ump.edu.my/id/eprint/3663/1/44ICoCSIM.pdf
first_indexed 2023-09-18T21:58:04Z
last_indexed 2023-09-18T21:58:04Z
_version_ 1777414212638736384

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