TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems
Scratchpad memory (SPM) is widely utilized in many embedded systems as a software-controlled on-chip memory to replace the traditional cache. New non-volatile memory (NVM) has emerged as a promising candidate to replace SRAM in SPM, due to its significant benefits, such as low-power consumption and...
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| Format: | Article |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8476592/ |
| _version_ | 1818853845762048000 |
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| author | Linbo Long Qing Ai Xiaotong Cui Jun Liu |
| author_facet | Linbo Long Qing Ai Xiaotong Cui Jun Liu |
| author_sort | Linbo Long |
| collection | DOAJ |
| description | Scratchpad memory (SPM) is widely utilized in many embedded systems as a software-controlled on-chip memory to replace the traditional cache. New non-volatile memory (NVM) has emerged as a promising candidate to replace SRAM in SPM, due to its significant benefits, such as low-power consumption and high performance. In particular, several representative NVMs, such as PCM, ReRAM, and STT-RAM can build multiple-level cells (MLC) to achieve even higher density. Nevertheless, this triggers off higher energy overhead and longer access latency compared with its single-level cell (SLC) counterpart. To address this issue, this paper first proposes a specific SPM with morphable NVM, in which the memory cell can be dynamically programmed to the MLC mode or SLC mode. Considering the benefits of high-density MLC and low-energy SLC, a simple and novel optimization technique, named theory of thermal expansion and contraction, is presented to minimize the energy consumption and access latency in embedded systems. The basic idea is to dynamically adjust the size configure of SLC/MLC in SPM according to the different workloads of program and allocate the optimal storage medium for each data. Therefore, an integer linear programming formulation is first built to produce an optimal SLC/MLC SPM partition and data allocation. In addition, a corresponding approximation algorithm is proposed to achieve near-optimal results in polynomial time. Finally, the experimental results show that the proposed technique can effectively improve the system performance and reduce the energy consumption. |
| first_indexed | 2024-12-19T07:43:17Z |
| format | Article |
| id | doaj.art-ff9c08b3c8ac42749cdc6f4b065ff938 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:43:17Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ff9c08b3c8ac42749cdc6f4b065ff9382022-12-21T20:30:25ZengIEEEIEEE Access2169-35362018-01-016547015471210.1109/ACCESS.2018.28727628476592TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded SystemsLinbo Long0https://orcid.org/0000-0003-1966-0714Qing Ai1Xiaotong Cui2Jun Liu3College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of Software and Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, ChinaScratchpad memory (SPM) is widely utilized in many embedded systems as a software-controlled on-chip memory to replace the traditional cache. New non-volatile memory (NVM) has emerged as a promising candidate to replace SRAM in SPM, due to its significant benefits, such as low-power consumption and high performance. In particular, several representative NVMs, such as PCM, ReRAM, and STT-RAM can build multiple-level cells (MLC) to achieve even higher density. Nevertheless, this triggers off higher energy overhead and longer access latency compared with its single-level cell (SLC) counterpart. To address this issue, this paper first proposes a specific SPM with morphable NVM, in which the memory cell can be dynamically programmed to the MLC mode or SLC mode. Considering the benefits of high-density MLC and low-energy SLC, a simple and novel optimization technique, named theory of thermal expansion and contraction, is presented to minimize the energy consumption and access latency in embedded systems. The basic idea is to dynamically adjust the size configure of SLC/MLC in SPM according to the different workloads of program and allocate the optimal storage medium for each data. Therefore, an integer linear programming formulation is first built to produce an optimal SLC/MLC SPM partition and data allocation. In addition, a corresponding approximation algorithm is proposed to achieve near-optimal results in polynomial time. Finally, the experimental results show that the proposed technique can effectively improve the system performance and reduce the energy consumption.https://ieeexplore.ieee.org/document/8476592/Data allocationscratchpad memorymorphable NVMembedded systems |
| spellingShingle | Linbo Long Qing Ai Xiaotong Cui Jun Liu TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems IEEE Access Data allocation scratchpad memory morphable NVM embedded systems |
| title | TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems |
| title_full | TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems |
| title_fullStr | TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems |
| title_full_unstemmed | TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems |
| title_short | TTEC: Data Allocation Optimization for Morphable Scratchpad Memory in Embedded Systems |
| title_sort | ttec data allocation optimization for morphable scratchpad memory in embedded systems |
| topic | Data allocation scratchpad memory morphable NVM embedded systems |
| url | https://ieeexplore.ieee.org/document/8476592/ |
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