  Many neural network quantization techniques have been developed to decrease
the computational and memory footprint of deep learning. However, these methods
are evaluated subject to confounding tradeoffs that may affect inference
acceleration or resource complexity in exchange for higher accuracy. In this
work, we articulate a variety of tradeoffs whose impact is often overlooked and
empirically analyze their impact on uniform and mixed-precision post-training
quantization, finding that these confounding tradeoffs may have a larger impact
on quantized network accuracy than the actual quantization methods themselves.
Because these tradeoffs constrain the attainable hardware acceleration for
different use-cases, we encourage researchers to explicitly report these design
choices through the structure of "quantization cards." We expect quantization
cards to help researchers compare methods more effectively and engineers
determine the applicability of quantization techniques for their hardware.
