Method and system for two-step hierarchical model optimization

Abstract

State of art approaches independently use a Pruning-weight Clustering-Quantization (PCQ) or Knowledge Distillation (KD) for model optimization and require critical manual intervention. Embodiments of the present disclosure provide a method and system for the two-step hierarchical model optimization approach for generating optimized model DL model. The method comprises a AutoPCQ technique followed by conditional application of an automated KD (AKD) technique. The AutoPCQ technique formulates a problem of configuration selection of the DL model as an optimization problem by iteratively applying Bayesian optimization and Reinforcement Learning. Further, the AKD technique formulates automated search of a student model as the optimization problem with the DL model representing a teacher model. A search space for the student model is defined by a restricted Neural Network Architecture Search that restricts the search space. The method automates the model optimization, in time efficient manner without compromising accuracy of the optimized model.