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Dynamic energy-aware fixed-point linear mapping multiplier for internet of things edge devices
Dongling Wu1, Likun Zhao2, Bonghwan Kim3
1Department of Electronic Engineering, Daegu Catholic University, Hayang-eup, Gyeongsan-si, Gyeongbuk, 38430, South Korea.
Scientific Reports
|June 2, 2026
Summary
This study introduces dynamic approximate multipliers for IoT edge systems, enabling adaptive power savings up to 70% by adjusting accuracy based on real-time energy availability. These energy-aware designs significantly reduce consumption in sensor nodes.
Area of Science:
- Computer Engineering
- Embedded Systems
- Energy Harvesting
Background:
- Approximate computing reduces power in error-tolerant applications, crucial for resource-constrained Internet of Things (IoT) edge systems.
- Existing static approximate multipliers lack adaptability to dynamic energy conditions at runtime.
- Runtime energy monitoring and adaptive approximation control are needed for efficient edge computing.
Purpose of the Study:
- To propose a Dynamic Energy-Aware Linear Mapping Multiplier (DEA-LMM) for adaptive accuracy-energy trade-offs in IoT systems.
- To introduce an enhanced Dynamic Energy-Aware Linear Improved Mapping Multiplier (DEA-ILMM) with dynamic truncation for further energy efficiency.
- To enable flexible and effective energy-adaptive computation in modern edge systems.
Main Methods:
- Developed DEA-LMM integrating runtime energy monitoring and adaptive approximation control with energy-aware normalization and multi-level compensation.
- Introduced DEA-ILMM extending DEA-LMM with energy-aware dynamic truncation for adaptive operand precision.
- Evaluated designs on FPGA and ASIC platforms using solar-energy-harvesting traces and real-world validation on an ESP32-C3 sensor node.
Main Results:
- DEA-LMM and DEA-ILMM achieve up to 70% power savings in aggressive modes.
- Runtime dynamic adaptation provides an additional 15-25% energy reduction compared to static approximate multipliers.
- Real-world validation demonstrated a 47% energy reduction with negligible accuracy loss in environmental monitoring.
Conclusions:
- The proposed DEA-LMM and DEA-ILMM offer a flexible and effective solution for energy-adaptive computation in edge systems.
- Dynamic adjustment of the accuracy-energy trade-off is crucial for optimizing performance under varying energy budgets.
- These designs are compatible with fixed-point linear mapping and avoid core multiplier structure modifications.
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