DRAC: a delta recurrent neural network-based arithmetic coding algorithm for edge computing

This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experi...

Full description

Saved in:
Bibliographic Details
Published in:Complex & intelligent systems Vol. 8; no. 5; pp. 3675 - 3681
Main Authors: Shan, Bowei, Fang, Yong
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.10.2022
Springer Nature B.V
Subjects:
Algorithms
Arithmetic coding
Complexity
Computational Intelligence
Computer networks
Data compression
Data Structures and Information Theory
Datasets
Edge computing
Engineering
Intelligent systems
Internet of Things
Neural networks
Original Article
Power consumption
Recurrent neural networks
Arithmetic coding
Delta recurrent neural network
Edge computing
ISSN:2199-4536, 2198-6053
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experimental results show that DRAC outperforms DeepZip and achieves 5X speedup ratio and 20X power consumption saving.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-021-00455-1