  Neural networks are essential components of learning-based software systems.
However, their high compute, memory, and power requirements make using them in
low resources domains challenging. For this reason, neural networks are often
quantized before deployment. Existing quantization techniques tend to degrade
the 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.
