CEG4N: Counter-Example Guided Neural Network Quantization Refinement

João Batista Pereira Matos Jr., Iury Bessa, Edoardo Manino, Xidan Song, Lucas C. Cordeiro

Abstract: Neural networks are essential components of learning-based software systems. However, deploying neural networks in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, neural networks are often quantized before deployment, but existing quantization techniques tend to degrade network accuracy. We propose Counter-Example Guided Neural Network Quantization Refinement (CEG4N). This technique combines search-based quantization and equivalence verification: the former minimizes the computational requirements, while the latter guarantees that the network’s output does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small networks. Our technique successfully quantizes the networks in our evaluation while producing models with up to 72% better accuracy than state-of-the-art techniques.