Edge computing environments present unique challenges for neural network deployment due to resource constraints and latency requirements. This paper explores optimized deployment strategies for neural networks in edge computing scenarios, focusing on model compression techniques, adaptive allocation algorithms, and dynamic resource management. We propose a novel framework that combines quantization, pruning, and knowledge distillation to create lightweight models without significant accuracy loss. Experimental results demonstrate that our approach reduces model size by up to 70% while maintaining 95% of original accuracy. The framework also includes an adaptive scheduler that dynamically redistributes computational loads based on current network conditions and task priorities. Our evaluation across multiple edge devices shows an average latency reduction of 40% compared to traditional deployment methods. These findings contribute to more efficient and practical implementations of artificial intelligence in resource-constrained environments, enabling real-time applications in IoT, autonomous systems, and smart cities.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Edge computing environments present unique challenges for neural network deployment due to resource constraints and latency requirements. This paper explores optimized deployment strategies for neural networks in edge computing scenarios, focusing on model compression techniques, adaptive allocation algorithms, and dynamic resource management. We propose a novel framework that combines quantization, pruning, and knowledge distillation to create lightweight models without significant accuracy loss. Experimental results demonstrate that our approach reduces model size by up to 70% while maintaining 95% of original accuracy. The framework also includes an adaptive scheduler that dynamically redistributes computational loads based on current network conditions and task priorities. Our evaluation across multiple edge devices shows an average latency reduction of 40% compared to traditional deployment methods. These findings contribute to more efficient and practical implementations of artificial intelligence in resource-constrained environments, enabling real-time applications in IoT, autonomous systems, and smart cities.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
The rapid advancement of artificial intelligence, particularly in large language models, has brought significant challenges in decoding methods that balance performance with interpretability. This paper presents a comprehensive analysis of advanced decoding techniques that enhance both model capabilities and explainability. We explore novel approaches including adaptive temperature sampling, nucleus sampling with dynamic thresholds, and hybrid decoding methods that combine multiple strategies. Our research demonstrates that these advanced techniques can significantly improve model performance metrics while maintaining or enhancing interpretability. Through extensive experimentation across diverse datasets, we show that our proposed hybrid decoding method achieves a 12.3% improvement in perplexity scores while maintaining competitive computational efficiency. The findings contribute to the growing body of research on transparent AI systems and provide practical insights for practitioners aiming to deploy more reliable and interpretable AI models in high-stakes applications.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
The rapid advancement of artificial intelligence, particularly in large language models, has brought significant challenges in decoding methods that balance performance with interpretability. This paper presents a comprehensive analysis of advanced decoding techniques that enhance both model capabilities and explainability. We explore novel approaches including adaptive temperature sampling, nucleus sampling with dynamic thresholds, and hybrid decoding methods that combine multiple strategies. Our research demonstrates that these advanced techniques can significantly improve model performance metrics while maintaining or enhancing interpretability. Through extensive experimentation across diverse datasets, we show that our proposed hybrid decoding method achieves a 12.3% improvement in perplexity scores while maintaining competitive computational efficiency. The findings contribute to the growing body of research on transparent AI systems and provide practical insights for practitioners aiming to deploy more reliable and interpretable AI models in high-stakes applications.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations