  The post-training quantization (PTQ) challenge of bringing quantized neural
net accuracy close to original has drawn much attention driven by industry
demand. Many of the methods emphasize optimization of a specific
degree-of-freedom (DoF), such as quantization step size, preconditioning
factors, bias fixing, often chained to others in multi-step solutions. Here we
rethink quantized network parameterization in HW-aware fashion, towards a
unified analysis of all quantization DoF, permitting for the first time their
joint end-to-end finetuning. Our single-step simple and extendable method,
dubbed quantization-aware finetuning (QFT), achieves 4-bit weight quantization
results on-par with SoTA within PTQ constraints of speed and resource.
