Ultra-Low Latency AI Systems: Leveraging Edge AI and Semiconductor Acceleration for Local Language Model Inference
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P140Keywords:
Edge Artificial Intelligence, Ultra Low Latency Systems, Semiconductor Acceleration, Local Language Models, On Device AI Inference, Neural Processing Units, Edge Computing Architecture, Hardware Accelerated Machine LearningAbstract
Artificial intelligence applications increasingly rely on language models capable of understanding and generating natural language in real time. However, most large language models are typically deployed through cloud-based infrastructures, where network communication, bandwidth limitations, and data transfer delays introduce latency that constrains time-sensitive applications. These limitations have motivated growing interest in performing AI inference directly on edge devices, where computation occurs closer to the data source. At the same time, recent advances in semiconductor design, including neural processing units, application-specific integrated circuits, and specialized AI accelerators, have significantly improved the feasibility of executing complex models on resource-constrained hardware. This study examines how the integration of Edge AI architectures with semiconductor acceleration can enable ultra-low latency inference for locally deployed language models. The paper proposes a hardware-aware system architecture that combines optimized language models with dedicated AI accelerators to support efficient on-device inference. Model optimization strategies, including quantization and parameter reduction, are incorporated to accommodate the computational constraints of edge platforms without significantly degrading performance. A comparative evaluation framework is developed to analyze latency, throughput, and energy efficiency across different deployment environments. Experimental analysis demonstrates that edge-based inference supported by semiconductor accelerators can substantially reduce response latency while maintaining stable throughput and improved energy efficiency compared with conventional cloud-based approaches. These findings highlight the practical viability of deploying compact language models directly on edge devices for real-time intelligent systems. The proposed framework guides the design of future AI systems that require rapid response times, enhanced privacy, and reduced dependence on centralized infrastructure. Applications such as smart surveillance, autonomous robotics, mobile assistants, and industrial monitoring systems can particularly benefit from these advancements.
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