Dataset for Enhancing State Retention With Energy-Efficient Memory Tracing in Intermittent Systems

This dataset supports the publication "Enhancing State Retention with Energy-Efficient Memory Tracing in Intermittent Systems," to be published in the IEEE Transactions on Sustainable Computing (T-SUSC).
DOI: 10.1109/TSUSC.2025.3621509

The dataset contains experimental results from the proposed memory tracing system (MeTra) for energy-efficient state retention in intermittently powered embedded platforms. Results include behavior during different workloads, granularity settings, RAM-to-NVM transfer times, energy measurements, and FPGA resource utilization with and without MeTra enabled.

All data are provided in a single Excel file (MeTra_Dataset.xlsx). Each sheet corresponds to a figure or table presented in the paper:

Sheet ‘Fig. 5’ — Data supporting Fig. 5: Behaviour of MeTra when running matrix multiplication with a RAM block size (granularity value) of 4 bytes, where 18.21% of the RAM was utilized, showing voltage threshold updates after each counter overflow interrupt.

Sheet ‘Fig. 6’ — Data supporting Fig. 6: Main memory (RAM) utilization and corresponding save time to NVM for granularity values of 4 and 8 bytes.

Sheet ‘Fig. 7’ — Data supporting Fig. 7: Comparative analysis of trace memory usage and RAM-to-NVM save duration at various stages of computational progress (% of application run) for matrix multiplication, array sorting, and AES, evaluated at granularities of 4 and 8 bytes.

Sheet ‘Fig. 8’ — Data supporting Fig. 8: Comparative analysis of trace memory usage and RAM-to-NVM save duration across multiple iterations of matrix multiplication, array sorting, and AES at granularities of 4 and 8 bytes.

Sheet ‘Fig. 9’ — Data supporting Fig. 9: Energy consumption to save selected RAM to NVM during state saving.

Sheet ‘Table II’ — Data supporting TABLE II: Resource utilization by the system, with and without the memory tracing system (MeTra).

File format: Microsoft Excel Workbook (.xlsx)

Licence: CC BY 4.0