Accelerating floating-point to fixed-point data type conversion with evolutionary algorithms

The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tu...

Full description

Saved in:
Bibliographic Details
Published in:Electronics letters Vol. 51; no. 3; pp. 244 - 246
Main Authors: Rosa, L.S, Toledo, C.F.M, Bonato, V
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 05.02.2015
Subjects:
accumulative arithmetic computation error
Algorithms
Arithmetic
combinatorial optimisation problem
Converting
data type representation
Evolutionary algorithms
evolutionary computation
Floating point arithmetic
floating‐point to fixed‐point data type conversion
Format
Hardware
Information and communications
maximum clock frequency
mobile robotic scenario
mobile robots
Optimization
power consumption
evolutionary computation
maximum clock frequency
mobile robotic scenario
evolutionary algorithms
combinatorial optimisation problem
mobile robots
floating-point to fixed-point data type conversion
floating point arithmetic
accumulative arithmetic computation error
power consumption
data type representation
ISSN:0013-5194, 1350-911X, 1350-911X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The choice of the data type representation has significant impacts on the resource utilisation, maximum clock frequency and power consumption of any hardware design. Although arithmetic hardware units for the fixed-point format can improve performance and reduce energy consumption, the process of tuning the right bit length is known as a time-consuming task, since it is a combinatorial optimisation problem guided by the accumulative arithmetic computation error. A novel evolutionary approach to accelerate the process of converting algorithms from the floating-point to fixed-point format is presented. Results are demonstrated by converting three computing-intensive algorithms from the mobile robotic scenario, where data error accumulated during execution is influenced by external factors, such as sensor noise and navigation environment characteristics. The proposed evolutionary algorithm accelerated the conversion process by up to 2.5 × against the state-of-the-art methods, allowing even further bit-length optimisations.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2014.3791