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<p>Approximate computing has emerged as an effective approach for reducing power consumption in error-tolerant applications, particularly in resource-constrained Internet of Things (IoT) edge systems. However, most existing approximate multipliers employ static approximation levels, limiting their ability to adapt to dynamically varying energy conditions at runtime. This paper proposes a Dynamic Energy-Aware Linear Mapping Multiplier (DEA-LMM) that integrates runtime energy monitoring with adaptive approximation control. By incorporating energy-aware normalization, leading zero detection, and configurable multi-level compensation mechanisms, the proposed architecture enables dynamic adjustment of the accuracy–energy trade-off according to the available energy budget. To further improve energy efficiency, an enhanced architecture, referred to as Dynamic Energy-Aware Linear Improved Mapping Multiplier (DEA-ILMM), is introduced by extending the baseline design with energy-aware dynamic truncation. This mechanism adaptively configures operand precision at runtime, reducing power and area overhead while maintaining bounded approximation error. Both designs preserve compatibility with fixed-point linear mapping formulations and avoid modifications to the core multiplier structure. Comprehensive experimental evaluations on Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) platforms, including realistic solar-energy-harvesting traces, demonstrate that the proposed DEA-LMM and DEA-ILMM achieve up to 70% power savings in aggressive modes and an additional 15–25% energy reduction through runtime dynamic adaptation compared with state-of-the-art static approximate multipliers, while preserving acceptable accuracy across dynamically varying energy budgets. Real-world validation on an ESP32-C3-based solar-powered sensor node further confirms 47% energy reduction with negligible accuracy loss in practical environmental monitoring tasks. These results indicate that the proposed designs provide a flexible and effective solution for energy-adaptive computation in modern edge systems.</p>

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Dynamic energy-aware fixed-point linear mapping multiplier for internet of things edge devices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Dongling Wu,
  • Likun Zhao,
  • Bonghwan Kim,
  • Junjie Yin,
  • Jierui Xue

摘要

Approximate computing has emerged as an effective approach for reducing power consumption in error-tolerant applications, particularly in resource-constrained Internet of Things (IoT) edge systems. However, most existing approximate multipliers employ static approximation levels, limiting their ability to adapt to dynamically varying energy conditions at runtime. This paper proposes a Dynamic Energy-Aware Linear Mapping Multiplier (DEA-LMM) that integrates runtime energy monitoring with adaptive approximation control. By incorporating energy-aware normalization, leading zero detection, and configurable multi-level compensation mechanisms, the proposed architecture enables dynamic adjustment of the accuracy–energy trade-off according to the available energy budget. To further improve energy efficiency, an enhanced architecture, referred to as Dynamic Energy-Aware Linear Improved Mapping Multiplier (DEA-ILMM), is introduced by extending the baseline design with energy-aware dynamic truncation. This mechanism adaptively configures operand precision at runtime, reducing power and area overhead while maintaining bounded approximation error. Both designs preserve compatibility with fixed-point linear mapping formulations and avoid modifications to the core multiplier structure. Comprehensive experimental evaluations on Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC) platforms, including realistic solar-energy-harvesting traces, demonstrate that the proposed DEA-LMM and DEA-ILMM achieve up to 70% power savings in aggressive modes and an additional 15–25% energy reduction through runtime dynamic adaptation compared with state-of-the-art static approximate multipliers, while preserving acceptable accuracy across dynamically varying energy budgets. Real-world validation on an ESP32-C3-based solar-powered sensor node further confirms 47% energy reduction with negligible accuracy loss in practical environmental monitoring tasks. These results indicate that the proposed designs provide a flexible and effective solution for energy-adaptive computation in modern edge systems.