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As the complexity and computational demands of deep learning models rise, theneed for effective optimization methods for neural network designs becomesparamount. This work introduces an innovative search mechanism forautomatically selecting the best bit-width and layer-width for individualneural network layers. This leads to a marked enhancement in deep neuralnetwork efficiency. The search domain is strategically reduced by leveragingHessian-based pruning, ensuring the removal of non-crucial parameters.Subsequently, we detail the development of surrogate models for favorable andunfavorable outcomes by employing a cluster-based tree-structured Parzenestimator. This strategy allows for a streamlined exploration of architecturalpossibilities and swift pinpointing of top-performing designs. Through rigoroustesting on well-known datasets, our method proves its distinct advantage overexisting methods. Compared to leading compression strategies, our approachrecords an impressive 20% decrease in model size without compromising accuracy.Additionally, our method boasts a 12x reduction in search time relative to thebest search-focused strategies currently available. As a result, our proposedmethod represents a leap forward in neural network design optimization, pavingthe way for quick model design and implementation in settings with limitedresources, thereby propelling the potential of scalable deep learningsolutions.

Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation