Abstract
Scratchpad memory (SPM) occupies less space and consumes less energy than cache memory. However, unlike with cache memory, the user or the compiler must directly allocate the data or instructions. In this paper, we propose a new compiler based on the LLVM compiler that allocates frequently used global variables to the SPM and addresses issues in a multi-threaded environment with frequent I/O operations. The results demonstrate approximately a 27% reduction in energy consumption.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Region 10 Conference 2024 |
| Subtitle of host publication | Artificial Intelligence and Deep Learning Technologies for Sustainable Future, TENCON 2024 |
| Editors | Bin Luo, Sanjib Kumar Sahoo, Yee Hui Lee, Christopher H T Lee, Michael Ong, Arokiaswami Alphones |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 802-803 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798350350821 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE Region 10 Conference, TENCON 2024 - Singapore, Singapore Duration: 1 Dec 2024 → 4 Dec 2024 |
Publication series
| Name | IEEE Region 10 Annual International Conference, Proceedings/TENCON |
|---|---|
| ISSN (Print) | 2159-3442 |
| ISSN (Electronic) | 2159-3450 |
Conference
| Conference | 2024 IEEE Region 10 Conference, TENCON 2024 |
|---|---|
| Country/Territory | Singapore |
| City | Singapore |
| Period | 1/12/24 → 4/12/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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