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

TL;DR

This paper presents a novel, efficient method for optimizing neural network bit-width and layer width using a cluster-based tree-structured Parzen estimator, significantly reducing search time and model size while maintaining accuracy.

Contribution

It introduces a new search mechanism combining Hessian-based pruning and surrogate modeling for rapid, effective neural network architecture optimization.

Findings

20% reduction in model size without accuracy loss

12x faster search compared to existing methods

Effective optimization on standard datasets

Abstract

As the complexity and computational demands of deep learning models rise, the need for effective optimization methods for neural network designs becomes paramount. This work introduces an innovative search mechanism for automatically selecting the best bit-width and layer-width for individual neural network layers. This leads to a marked enhancement in deep neural network efficiency. The search domain is strategically reduced by leveraging Hessian-based pruning, ensuring the removal of non-crucial parameters. Subsequently, we detail the development of surrogate models for favorable and unfavorable outcomes by employing a cluster-based tree-structured Parzen estimator. This strategy allows for a streamlined exploration of architectural possibilities and swift pinpointing of top-performing designs. Through rigorous testing on well-known datasets, our method proves its distinct advantage over existing methods. Compared to leading compression strategies, our approach records an impressive 20% decrease in model size without compromising accuracy. Additionally, our method boasts a 12x reduction in search time relative to the best search-focused strategies currently available. As a result, our proposed method represents a leap forward in neural network design optimization, paving the way for quick model design and implementation in settings with limited resources, thereby propelling the potential of scalable deep learning solutions.