AAAE: Application Accuracy-Aware Evaluation Framework for Approximate Computing

Approximate computing offers an effective approach to reducing the hardware cost of neural inference, but efficiently characterizing model-specific error tolerance across large approximate multiplier design spaces remains challenging. We present AAAE, an application accuracy-aware evaluation framework that guides approximate multiplier design under explicit accuracy constraints. AAAE combines multi-scale and model-conditioned error features, augmented with lightweight inference responses for Transformers, to predict the accuracy degradation of previously unseen multiplier designs. In parallel, a graph neural network estimates hardware costs from multiplier structures. These predictions, together with empirically calibrated accuracy bounds, guide design optimization, with selected designs verified through inference and high-fidelity hardware evaluation. Experiments on three vision Transformers and two CNNs demonstrate Top-1 accuracy-drop prediction mean absolute errors of 0.43–0.59 percentage points. The hardware surrogate achieves a power–delay product prediction mean relative error of 0.241%. In the reported timing setting, surrogate evaluation provides a 35.1× per-design speedup over 1,000-image accuracy evaluation plus high-fidelity hardware evaluation. Under a two-percentage-point accuracy-loss budget, the calibrated bound A1 achieves an empirical false-discovery proportion of at most 1% across all five models. Our implementation is publicly available at https://anonymous.4open.science/r/AAAE-7CB0/.