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Quantization is a widely used technique to compress neural networks.Assigning uniform bit-widths across all layers can result in significantaccuracy degradation at low precision and inefficiency at high precision.Mixed-precision quantization (MPQ) addresses this by assigning variedbit-widths to layers, optimizing the accuracy-efficiency trade-off. Existingsensitivity-based methods for MPQ assume that quantization errors across layersare independent, which leads to suboptimal choices. We introduce CLADO, apractical sensitivity-based MPQ algorithm that captures cross-layer dependencyof quantization error. CLADO approximates pairwise cross-layer errors usinglinear equations on a small data subset. Layerwise bit-widths are assigned byoptimizing a new MPQ formulation based on cross-layer quantization errors usingan Integer Quadratic Program. Experiments with CNN and vision transformermodels on ImageNet demonstrate that CLADO achieves state-of-the-artmixed-precision quantization performance. Code repository available here:https://github.com/JamesTuna/CLADO_MPQ

Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming