In the rapidly evolving world of Artificial Intelligence, bigger isn't always better. While Large Language Models (LLMs) have dominated the landscape, a new breed of Small Language Models (SLMs) is emerging, promising similar performance with a fraction of the computational cost. These smaller, nimbler models are ideal for deployment on resource-constrained devices like smartphones and embedded systems. However, even SLMs can be computationally intensive. Researchers have been exploring innovative ways to further optimize these models, leading to a breakthrough technique called 'sparse activation.' This method selectively activates only the most essential neurons during the AI's thinking process, allowing for significant energy and memory savings without sacrificing accuracy. The challenge lies in identifying which neurons are truly crucial. Traditional methods based on neuron output magnitudes have proven ineffective for SLMs. This research introduces a novel approach, leveraging 'attribution scores' to pinpoint the most influential neurons. These scores essentially measure each neuron's contribution to the final output, providing a more precise measure of its importance. However, these scores can be misleading due to complex interdependencies between neurons across different layers of the model. The researchers address this by developing a 'corrective term' that compensates for these dependencies, resulting in highly accurate sparse activation. Experiments with popular SLMs like Phi and MobiLlama, on challenging question-answering datasets, demonstrate remarkable results. The new technique achieves up to 80% sparsity—meaning only 20% of the neurons are active—with a minimal accuracy drop of less than 5%. This discovery opens exciting doors for deploying powerful AI capabilities on everyday devices, paving the way for a future where AI is more accessible and efficient than ever before. This new approach not only enhances efficiency but also provides insights into the inner workings of AI models, revealing how different parts of the model contribute to different aspects of language understanding and generation.