ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers

Researchers introduce ScalePredictor, a dynamic quantization framework that optimizes Vision Transformer deployment on edge devices by learning instance-aware quantization scales. The method leverages correlations between shallow-layer activation distributions and deeper-layer optimal scales, achieving superior accuracy-efficiency trade-offs compared to existing post-training quantization approaches.

ScalePredictor addresses a critical bottleneck in deploying Vision Transformers to resource-constrained devices. While ViTs have demonstrated exceptional performance across computer vision tasks, their computational intensity creates barriers to edge deployment. Traditional post-training quantization applies uniform compression across all input samples, ignoring the inherent variability in activation distributions across different images.

The innovation lies in recognizing that shallow layers contain predictive information about optimal quantization scales for deeper layers. By extracting robust range statistics early in the network and using polynomial approximation to project these statistics into per-layer scales, ScalePredictor achieves dynamic, sample-aware quantization with minimal computational overhead. This approach contrasts sharply with just-in-time calibration methods that require expensive per-instance computations.

For the broader AI infrastructure landscape, this work has tangible implications for real-world deployment scenarios. Edge devices power mobile applications, IoT systems, and autonomous vehicles—domains where inference latency and power consumption directly impact user experience and operational costs. By improving the accuracy-efficiency frontier of quantized ViTs, ScalePredictor reduces the engineering burden for practitioners deploying vision models at scale.

The research demonstrates strong empirical results on ImageNet, establishing new performance baselines for PTQ methods. As Vision Transformers increasingly replace CNNs in production systems, quantization techniques become essential infrastructure. Future developments may explore adaptive quantization strategies across different hardware targets or integration with other compression techniques like pruning and knowledge distillation.

• →ScalePredictor enables dynamic quantization of Vision Transformers by predicting optimal scales from shallow-layer activation statistics
• →The method achieves better accuracy-efficiency trade-offs than existing post-training quantization approaches with negligible computational overhead
• →Correlation discovery between shallow and deep layer distributions provides a principled foundation for instance-aware scale learning
• →Polynomial approximation eliminates costly just-in-time calibration while maintaining quantization quality across diverse input samples
• →Results on ImageNet establish new performance standards for quantized Vision Transformer deployment on edge devices