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

Intermittent systems powered by harvested energy frequently encounter power outages, requiring efficient mechanisms to save and restore their internal state, to ensure computational progress. In these systems, minimising the overhead of state retention, comprising CPU core registers and main volatile memory contents (a snapshot), is essential to maximise computational progress within the constraints of limited energy availability. This paper introduces a hardware module, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> (MEmory TRAcing), designed to enhance energy-efficient state retention in intermittent systems. This is achieved by tracing and selectively saving the modified parts of the main volatile memory (RAM) to non-volatile memory (NVM), i.e. FRAM. Additionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> dynamically adjusts the voltage threshold that initiates state saving, optimizing energy usage for each snapshot and enabling the system to dedicate more harvested energy to useful computations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> was integrated with an Arm Cortex-M1 processor on an FPGA and evaluated using benchmarks including matrix multiplication, array sorting, and advanced encryption standard (AES), demonstrating its effectiveness in reducing state-saving time and improving energy efficiency by selectively saving modified RAM regions instead of the entire memory. Experimental results demonstrate that snapshot time can be reduced by 48.34% to 57.56% in FRAM-based systems, leading to an improvement in energy efficiency of 65.24% to 77.76%. These gains are achieved by selectively saving 5.66% to 19.82% of RAM, using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i>, compared to saving entire RAM.

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Publication Details

Journal
IEEE Transactions on Sustainable Computing
Published
2025-10-14
DOI
https://doi.org/10.1109/tsusc.2025.3621509
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

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

Geoff V. Merrett, Theodoros D. Verykios, Domenico Balsamo, Osama Bin Tariq
IEEE Transactions on Sustainable Computing
Advanced Memory and Neural Computing
article

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

Geoff V. Merrett, Theodoros D. Verykios, Domenico Balsamo, Osama Bin Tariq
article en

Abstract

Intermittent systems powered by harvested energy frequently encounter power outages, requiring efficient mechanisms to save and restore their internal state, to ensure computational progress. In these systems, minimising the overhead of state retention, comprising CPU core registers and main volatile memory contents (a snapshot), is essential to maximise computational progress within the constraints of limited energy availability. This paper introduces a hardware module, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> (MEmory TRAcing), designed to enhance energy-efficient state retention in intermittent systems. This is achieved by tracing and selectively saving the modified parts of the main volatile memory (RAM) to non-volatile memory (NVM), i.e. FRAM. Additionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> dynamically adjusts the voltage threshold that initiates state saving, optimizing energy usage for each snapshot and enabling the system to dedicate more harvested energy to useful computations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i> was integrated with an Arm Cortex-M1 processor on an FPGA and evaluated using benchmarks including matrix multiplication, array sorting, and advanced encryption standard (AES), demonstrating its effectiveness in reducing state-saving time and improving energy efficiency by selectively saving modified RAM regions instead of the entire memory. Experimental results demonstrate that snapshot time can be reduced by 48.34% to 57.56% in FRAM-based systems, leading to an improvement in energy efficiency of 65.24% to 77.76%. These gains are achieved by selectively saving 5.66% to 19.82% of RAM, using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MeTra</i>, compared to saving entire RAM.

IEEE Transactions on Sustainable ComputingVol. 10(6)
University of Southampton (GB), Newcastle University (GB)
Openalex Percentile: Top 71%
Advanced Memory and Neural Computing
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