Edge intelligence has emerged as a key enabler for real-time analytics in Internet of Things (IoT), smart healthcare, autonomous systems, and industrial monitoring. However, deploying Transformer-based models at the edge is challenging due to constrained computational power, memory, and energy availability. This paper presents a study on lightweight Transformer models for edge intelligence under constrained environments, focusing on architectural optimization and model compression techniques. The proposed approach integrates parameter sharing, reduced attention heads, low-rank projection, and quantization-aware training to minimize resource usage while preserving accuracy. Experimental evaluation on benchmark edge datasets demonstrates that the optimized lightweight Transformer achieves up to 48% reduction in model size, 42% lower inference latency, and 35% lower energy consumption compared to standard Transformer models. Despite these reductions, the model maintains competitive performance, achieving 94.1% accuracy, with only a 1.8% accuracy drop relative to full-scale models. Furthermore, real-time inference throughput improved by 1.6× on edge devices such as Raspberry Pi and NVIDIA Jetson Nano. The results confirm that carefully designed lightweight Transformers can effectively balance accuracy, efficiency, and responsiveness, making them suitable for deployment in resource-constrained edge environments.

This study presented LT-Edge, a lightweight Transformer-based framework designed to enable efficient edge intelligence under constrained computational, memory, and energy environments. By integrating reduced-head low-rank attention, compressed feed-forward networks, and edge-aware optimization through quantization-aware training, the proposed model successfully addresses the limitations of deploying conventional Transformer architectures on edge devices. Experimental results demonstrate that LT-Edge achieves substantial reductions in model size, inference latency, and energy consumption while maintaining high predictive accuracy comparable to full-scale Transformer models. The balanced trade-off between efficiency and performance makes LT-Edge suitable for real-time edge applications such as anomaly detection, classification, and time-series prediction. Furthermore, the modular design of LT-Edge allows adaptability across diverse edge hardware platforms, enhancing its scalability and practical applicability. Overall, the findings confirm that carefully designed lightweight Transformer architectures can deliver robust and responsive intelligence at the edge, paving the way for widespread adoption of Transformer-based models in next-generation resource-constrained edge computing systems.

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