The three-way intersection of on-device AI inference optimization, retrieval-augmented generation, and Green AI / sustainability has not previously been drawn together into a unified system-design perspective, and this survey undertakes that cross-domain synthesis.
The hypothesis that native SLMs must be the most sustainable edge choice is not supported universally; optimized quantized LLMs can win overall, while SLMs remain competitive through lower resource demand.
A systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases, and reveals that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks.
Marco Fiore, Francesca Lanera· Electronics· 1 citation
SmartRAG is presented, a fully on-device framework that organizes an intelligent assistant around four coordinated modules -- Perception, Memory, Focus, and Thinking -- keeping inference costs bounded and at the core of EvoNER, a continually learnable named-entity recognizer that incrementally expands its label invento...
Zhihan Jiang, Meng Li, Shenghao Liu et al.· arXiv.org· 0 citations
A two-axis taxonomy connecting six compression families to five semantic communication architectures is synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier, synthesized with a quantitative meta-analysis of the model-size-versus-semantic-fidelity Pareto frontier.
Srikanth Kamath, A. Mathur, Joslyn Sajan George et al.· 0 citations
This work surveys dozens of recent works that report compression results on real hardware and extracts practical deployment guidelines from them, and deploys compact language and image models on GPU, CPU, and Raspberry Pi platforms across question answering and image segmentation.
Subhransu Das, Jiaming Cheng, Arnav Kumar et al.· 0 citations
HYMELL is introduced, a hybrid three-level framework for estimating LLM inference latency and energy by combining analytical modeling with machine learning (ML), which enables fast, hardware-free design space exploration and energy-efficient optimization.
Saeid Shokoufa, Mohammad Erfan Sadeghi, M. Kamal et al.· 0 citations
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