Optimizing data allocation for loops on embedded systems with scratch-pad memory

Jun Zhang; Tan Deng; Qiuyan Gao; Qingfeng Zhuge; Edwin H.M. Sha

doi:10.1109/RTCSA.2012.20

Optimizing data allocation for loops on embedded systems with scratch-pad memory

Jun Zhang^*
, Tan Deng
, Qiuyan Gao
, Qingfeng Zhuge
, Edwin H.M. Sha

^*此作品的通讯作者

Hunan University
Chongqing University
University of Texas at Dallas

科研成果: 会议稿件 › 论文 › 同行评审

9 引用（Scopus）

摘要

Scratch Pad Memory (SPM), a software-controlled on-chip memory, is popular in embedded systems due to its many benefits. To efficiently manage SPM, many different data allocation algorithms are proposed. However, most of them cannot achieve optimal results. In this paper, we proposed a dynamic programming approach, Iterational Optimal Data Allocation (IODA) to allocate data for embedded systems with multiple types of memory units. According to the experimental results, the IODA algorithm lowered the energy consumption by 20.14% and 5.11% compared to a random memory allocation and a greedy algorithm, respectively. It also reduced the memory access time by 18.44% and 5.83% compared to a random memory allocation and a greedy algorithm, respectively.

源语言	英语
页	184-191
页数	8
DOI	https://doi.org/10.1109/RTCSA.2012.20
出版状态	已出版 - 2012
已对外发布	是
活动	18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2012 - Seoul, 韩国期限: 19 8月 2012 → 22 8月 2012

会议

会议	18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2012
国家/地区	韩国
市	Seoul
时期	19/08/12 → 22/08/12

联合国可持续发展目标

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可持续发展目标 7 经济适用的清洁能源

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10.1109/RTCSA.2012.20

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@conference{9999ba4ee1c9473b96c4345d05c43a29,

title = "Optimizing data allocation for loops on embedded systems with scratch-pad memory",

abstract = "Scratch Pad Memory (SPM), a software-controlled on-chip memory, is popular in embedded systems due to its many benefits. To efficiently manage SPM, many different data allocation algorithms are proposed. However, most of them cannot achieve optimal results. In this paper, we proposed a dynamic programming approach, Iterational Optimal Data Allocation (IODA) to allocate data for embedded systems with multiple types of memory units. According to the experimental results, the IODA algorithm lowered the energy consumption by 20.14\% and 5.11\% compared to a random memory allocation and a greedy algorithm, respectively. It also reduced the memory access time by 18.44\% and 5.83\% compared to a random memory allocation and a greedy algorithm, respectively.",

keywords = "Data allocation, Loops, Optimization, Scratch-pad memory",

author = "Jun Zhang and Tan Deng and Qiuyan Gao and Qingfeng Zhuge and Sha, \{Edwin H.M.\}",

year = "2012",

doi = "10.1109/RTCSA.2012.20",

language = "英语",

pages = "184--191",

note = "18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2012 ; Conference date: 19-08-2012 Through 22-08-2012",

}

TY - CONF

T1 - Optimizing data allocation for loops on embedded systems with scratch-pad memory

AU - Zhang, Jun

AU - Deng, Tan

AU - Gao, Qiuyan

AU - Zhuge, Qingfeng

AU - Sha, Edwin H.M.

PY - 2012

Y1 - 2012

N2 - Scratch Pad Memory (SPM), a software-controlled on-chip memory, is popular in embedded systems due to its many benefits. To efficiently manage SPM, many different data allocation algorithms are proposed. However, most of them cannot achieve optimal results. In this paper, we proposed a dynamic programming approach, Iterational Optimal Data Allocation (IODA) to allocate data for embedded systems with multiple types of memory units. According to the experimental results, the IODA algorithm lowered the energy consumption by 20.14% and 5.11% compared to a random memory allocation and a greedy algorithm, respectively. It also reduced the memory access time by 18.44% and 5.83% compared to a random memory allocation and a greedy algorithm, respectively.

AB - Scratch Pad Memory (SPM), a software-controlled on-chip memory, is popular in embedded systems due to its many benefits. To efficiently manage SPM, many different data allocation algorithms are proposed. However, most of them cannot achieve optimal results. In this paper, we proposed a dynamic programming approach, Iterational Optimal Data Allocation (IODA) to allocate data for embedded systems with multiple types of memory units. According to the experimental results, the IODA algorithm lowered the energy consumption by 20.14% and 5.11% compared to a random memory allocation and a greedy algorithm, respectively. It also reduced the memory access time by 18.44% and 5.83% compared to a random memory allocation and a greedy algorithm, respectively.

KW - Data allocation

KW - Loops

KW - Optimization

KW - Scratch-pad memory

UR - https://www.scopus.com/pages/publications/84869078607

U2 - 10.1109/RTCSA.2012.20

DO - 10.1109/RTCSA.2012.20

M3 - 论文

AN - SCOPUS:84869078607

SP - 184

EP - 191

T2 - 18th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2012

Y2 - 19 August 2012 through 22 August 2012

ER -

Optimizing data allocation for loops on embedded systems with scratch-pad memory

摘要

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