MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compression

2025-06-03

AI Summary

Formal verification of compressed neural networks—particularly under quantization and pruning—for edge-device deployment suffers from low efficiency and poor adaptability to structural modifications. Method: We propose the first MUC (Minimal Unsatisfiable Core)-guided incremental formal verification framework, which uniformly models both parameter updates and architectural changes, enabling efficient reuse of prior verification knowledge. Our approach employs SMT encoding coupled with MUC-driven incremental solving to preserve functional equivalence while substantially accelerating verification. Contribution/Results: The framework achieves over 90% verification reuse rate and speeds up verification by multiple times compared to conventional non-incremental methods. Crucially, it overcomes a key limitation of existing incremental verifiers—which support only quantization and not structural pruning—thereby enabling rigorous, scalable, and reliable verification of diverse model compression techniques. This significantly enhances the trustworthiness and deployability of compressed models on resource-constrained edge devices.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The rapid development of deep learning has led to challenges in deploying neural networks on edge devices, mainly due to their high memory and runtime complexity. Network compression techniques, such as quantization and pruning, aim to reduce this complexity while maintaining accuracy. However, existing incremental verification methods often focus only on quantization and struggle with structural changes. This paper presents MUC-G4 (Minimal Unsat Core-Guided Incremental Verification), a novel framework for incremental verification of compressed deep neural networks. It encodes both the original and compressed networks into SMT formulas, classifies changes, and use emph{Minimal Unsat Cores (MUCs)} from the original network to guide efficient verification for the compressed network. Experimental results show its effectiveness in handling quantization and pruning, with high proof reuse rates and significant speedup in verification time compared to traditional methods. MUC-G4 hence offers a promising solution for ensuring the safety and reliability of compressed neural networks in practical applications.

Problem

• Verifying compressed deep neural networks efficiently
• Handling structural changes in network compression
• Ensuring safety of quantized and pruned networks

Innovation

• Uses Minimal Unsat Cores for verification guidance
• Encodes networks into SMT formulas for analysis
• Handles both quantization and pruning efficiently