A Lightweight Explainability Framework for Neural Networks: Methods, Benchmarks, and Mobile Deployment

Explainability is increasingly crucial for real-world deployment of deep learning models, yet traditional explanation techniques can be prohibitively slow and memory- intensive on resource-constrained devices. This paper presents a novel lightweight ex- plainability framework that significantly reduces the computational cost of generating explanations without compromising on quality. My approach focuses on an optimized Grad-CAM pipeline with sophisticated thresholding, advanced memory handling, and specialized evaluation metrics. I demonstrate speedups exceeding 300x over naive im- plementations while maintaining robust faithfulness and completeness scores. Through an extensive series of benchmarks, user studies, and statistical tests, I show that this framework is scalable, accurate, and deployable on edge devices such as Raspberry Pi, Android phones, and iPhones. I also discuss ethical considerations, future research directions, and potential applications in high-stakes domains like healthcare and au- tonomous systems.