FPGA Codec System of Learned Image Compression With Algorithm-Architecture Co-Optimization

IF 3.7 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-04-08 DOI:10.1109/JETCAS.2024.3386328
Heming Sun;Qingyang Yi;Masahiro Fujita
{"title":"FPGA Codec System of Learned Image Compression With Algorithm-Architecture Co-Optimization","authors":"Heming Sun;Qingyang Yi;Masahiro Fujita","doi":"10.1109/JETCAS.2024.3386328","DOIUrl":null,"url":null,"abstract":"Learned Image Compression (LIC) has shown a coding ability competitive to traditional standards. To address the complexity issue of LIC, various hardware accelerators are required. As one category of accelerators, FPGA has been used because of its good reconfigurability and high power efficiency. However, the prior work developed the algorithm of LIC neural network at first, and then proposed an associated FPGA hardware. This separate manner of algorithm and architecture development can easily cause a layout problem such as routing congestion when the hardware utilization is high. To mitigate this problem, this paper gives an algorithm-architecture co- optimization of LIC. We first restrict the input and output channel parallelism with some constraints to ease the routing issue with more DSP usage. After that, we adjust the numbers of channels to increase the DSP efficiency. As a result, compared with one recent work with a fine-grained pipelined architecture, we can reach up to 1.5x faster throughput with almost the same coding performance on the Kodak dataset. Compared with another recent work accelerated by AMD/Xilinx DPU, we can reach faster throughput with better coding performance.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10494759/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Learned Image Compression (LIC) has shown a coding ability competitive to traditional standards. To address the complexity issue of LIC, various hardware accelerators are required. As one category of accelerators, FPGA has been used because of its good reconfigurability and high power efficiency. However, the prior work developed the algorithm of LIC neural network at first, and then proposed an associated FPGA hardware. This separate manner of algorithm and architecture development can easily cause a layout problem such as routing congestion when the hardware utilization is high. To mitigate this problem, this paper gives an algorithm-architecture co- optimization of LIC. We first restrict the input and output channel parallelism with some constraints to ease the routing issue with more DSP usage. After that, we adjust the numbers of channels to increase the DSP efficiency. As a result, compared with one recent work with a fine-grained pipelined architecture, we can reach up to 1.5x faster throughput with almost the same coding performance on the Kodak dataset. Compared with another recent work accelerated by AMD/Xilinx DPU, we can reach faster throughput with better coding performance.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
算法-架构协同优化的学习图像压缩 FPGA 编解码器系统
学习图像压缩(LIC)已显示出可与传统标准相媲美的编码能力。为解决 LIC 的复杂性问题,需要各种硬件加速器。作为加速器的一种,FPGA 因其良好的可重构性和高能效而被广泛使用。然而,之前的工作都是先开发 LIC 神经网络的算法,然后再提出相关的 FPGA 硬件。这种算法和架构分开开发的方式很容易在硬件利用率较高时造成布局问题,如路由拥塞。为了缓解这一问题,本文给出了 LIC 的算法与架构协同优化方案。首先,我们通过一些约束条件限制输入和输出通道的并行性,从而在更多使用 DSP 的情况下缓解路由问题。然后,我们调整通道数量,以提高 DSP 效率。因此,与最近一项采用细粒度流水线架构的研究相比,我们在柯达数据集上的编码性能几乎相同,但吞吐量却提高了 1.5 倍。与 AMD/Xilinx DPU 加速的另一项最新研究相比,我们的吞吐量更快,编码性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
8.50
自引率
2.20%
发文量
86
期刊介绍: The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.
期刊最新文献
Introducing IEEE Collabratec Table of Contents IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors IEEE Circuits and Systems Society Information IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1