ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

Explainability can improve model compression: The paper demonstrates that using explainability techniques (Layer-wise Relevance Propagation) to identify which weights matter most allows for more effective quantization, meaning AI systems can be made smaller and faster while maintaining accuracy—suggesting that understanding why models work helps make them work better with fewer resources.

Ultra-low precision networks are achievable without sacrificing performance: ECQ^x successfully reduced neural networks to 2-5 bit precision (compared to standard 32-bit) while maintaining or even improving model performance, indicating that responsible AI deployment on resource-constrained devices like mobile phones doesn't necessarily require accepting lower accuracy.

Compression enables broader AI accessibility: By achieving up to 103x file size reduction through combining low-precision quantization with sparsity, this approach makes it feasible to deploy sophisticated AI models on devices with limited memory and computational power, potentially democratizing access to AI technology beyond data centers.

Responsible AI requires balancing multiple objectives: The method simultaneously optimizes for three goals—model accuracy, computational efficiency, and interpretability—demonstrating that responsible AI development often involves thoughtful trade-offs rather than optimizing for a single metric.

Explainability and efficiency are complementary, not competing goals: Rather than treating model interpretability and computational efficiency as separate concerns, this work shows they can reinforce each other, suggesting that building more trustworthy AI systems need not come at the cost of practical deployment constraints.