Editing Emerging Research Directions (section)

=== Hardware-Level Approaches ===

Research on hardware-focused strategies for reducing the carbon footprint at the edge has been extensive. Xu et al. examined system-on-chips (SoCs) designed specifically for energy efficiency, integrating ultra-low-power states and selective core activation [4]. Mendez and Ha evaluated heterogeneous multicore processors for embedded systems, highlighting the benefits of activating only the cores necessary to meet real-time performance requirements [5]. Similarly, the introduction of custom AI accelerators has been shown to yield significant power savings for neural network inference tasks [6].

Bae et al. emphasized that sustainable manufacturing practices and the use of recycled materials can reduce the overall lifecycle emissions of edge devices [7]. Kim et al. explored biologically inspired materials to enhance heat dissipation at the package level, while Liu and Zhang demonstrated that compact liquid-cooling solutions are viable even for micro data centers near the edge [8][9].


{| class="wikitable" style="width:100%; text-align:left;"
|+ Representative Hardware-Level Studies in Edge Computing
|-
! Study
! Key Focus
! Contributions
! Findings
|-
| [4] Xu et al.
| Ultra-low-power SoC design
| Introduced SoC with power gating and selective core activation
| Significant reduction in idle power consumption
|-
| [5] Mendez and Ha
| Heterogeneous multicore processors
| Evaluated activating only necessary cores for real-time tasks
| Improved balance of performance and energy usage
|-
| [6] Ramesh et al.
| Custom AI accelerators
| Developed specialized hardware for on-device inference
| Reported substantial energy savings in neural network operations
|-
| [7] Bae et al.
| Sustainable manufacturing
| Employed lifecycle assessments and recycled materials
| Achieved measurable decrease in manufacturing emissions
|-
| [8] Kim et al.
| Biologically inspired packaging
| Integrated biomimetic materials for enhanced heat dissipation
| Reduced cooling energy overhead and improved thermal performance
|-
| [9] Liu and Zhang
| Liquid cooling solutions
| Demonstrated compact liquid-cooling viability in micro data centers
| Quantitative improvement in cooling efficiency
|}