message: Reference our article where these concepts and computations were introduced, in the hope that the added context and explanations here are helpful to some readers.
diff: @@ -73,7 +73,10 @@ multiplied by dynamic input and activation values. This means that there is a
  unavoidable runtime cost of multiplying the zero-point of the weight with the
  activation value. By enforcing that zero-point is 0 we can avoid this cost.
  -Explanation of the math:
  +Explanation of the math: this is similar to section 2.3 in
  +[arXiv:1712.05877](https://arxiv.org/abs/1712.05877), except for the difference
  +that we allow the scale values to be per-axis. This generalizes readily, as
  +follows:
  $A$ is a $m \times n$ matrix of quantized activations. <br />
  $B$ is a $n \times p$ matrix of quantized weights. <br />
  @@ -539,3 +542,7 @@ QUANTIZE (Requantization)
  range : [-128, 127]
  granularity: per-tensor
  ```
  +
  +## References
  +
  +[arXiv:1712.05877](https://arxiv.org/abs/1712.05877)
  