This is the paper behind TensorFlow Lite quantization. The objective of the paper is to make inference of a machine learning model faster, especially on mobile devices. This problem can be solved using a network architecture that fits the computation and memory constraints, such as MobileNet, SqueezeNet, ShuffleNet, and DenseNet. Or it can be solved by reducing 32-bit floating point operations into lower bit-depth, such as in ternary weight networks and XNOR-net. This paper is on the latter approach.

Previous work on quantization focused only on the weights, i.e., to mitigate the on-device storage limitations. This, however, addresses the computation cost. Several situations are observed:

- floating point operations are expensive and most hardware can do integer arithmetic much faster
- multiplications are expensive but only when the operands are wide (e.g., 32-bit)
- power-of-2 multiplications can be done using bit shifts

The quantization in the paper is to find a bit representation \(q\) for real number \(r\). The real number \(r\) is indeed a 32-bit float, and the bit representation \(q\) is usually uint8. The conversion is affine transform:

\[r = S\times (q-Z)\]where \(S,Z\in\mathbb{R}\) are the quantization parameters. There is an additional requirement that \(q\) can represent exact zero value \(Z\).

The quantization is done on weight, bias, and activation. The weight and activation are usually unit8 while bias is int32. The weights in a neural network is usually involved in mult-add operations. Take, for example, multiplication of two square matrices \(R_3=R_1R_2\) which each element of the matrices are \(r_\alpha^{(i,j)}\), then by the rule of matrix multiplication

\[\begin{aligned} r_3^{(i,j)} &= \sum_{k=1}^N r_1^{(i,k)}r_2^{(k,j)} \\ S_3(q_3^{(i,j)} - Z_3) &= \sum_{k=1}^N S_1(q_1^{(i,k)} - Z_1) S_2(q_2^{(k,j)} - Z_2) \\ q_3^{(i,j)} &= Z_3 + \frac{S_1S_2}{S_3} \sum_{k=1}^N (q_1^{(i,k)} - Z_1) (q_2^{(k,j)} - Z_2) \\ &= Z_3 + \frac{S_1S_2}{S_3} \big( NZ_1Z_2 - Z_1\sum_{k=1}^N q_2^{(k,j)} - Z_2\sum_{k=1}^N q_1^{(i,k)} + \sum_{k=1}^N q_1^{(i,k)} q_2^{(k,j)} \big) \\ &= Z_3 + M \big( NZ_1Z_2 - Z_1\sum_{k=1}^N q_2^{(k,j)} - Z_2\sum_{k=1}^N q_1^{(i,k)} + \sum_{k=1}^N q_1^{(i,k)} q_2^{(k,j)} \big) \end{aligned}\]where \(M := S_1S_2/S_3\). The final equation above is significant because the first two summations are in \(O(N)\) only and across the entire matrix-matrix multiplication, there are totally \(2N^2\) such additions because it shares across the entire row or column. The last summation is unique to each \((i,j)\) and there are \(2N^3\) arithmetic operations in total. But these are all in uint8 so even the overall complexity is \(O(N^3)\), it is still fast.

The quantity \(M\) is a real number, empirically in the interval \((0,1)\). We can rewrite \(M\) as

\[M = 2^{-n}M_0\]which \(M_0\in [0.5,1)\). In this case, we can further make \(M_0\) an int32 with \(M_0 \ge 2^30\) and convert the multiplication by \(M\) an fixed-point multiplication (i.e., integer multiplication then bit shift). Therefore, the summation in the equations above should accumulate in int32 while the multiplication operands are in uint8. Similarly, it is natural to use int32 for bias as well. But also, using a bias of higher precision can help reducing quantization error. To facilitate addition of bias, we set

\[\begin{aligned} S_{\text{bias}} &= S_1S_2 \\ Z_{\text{bias}} &= 0 \end{aligned}\]The handling of activation function is interesting. The most common activation
function in deep networks are ReLU (or ReLU6, which is defined as
\(\max(0, \min(6, x))\)). It is indeed a saturating function. If we make the
quantization appropriately, we can simply make the *saturating cast* of int32
to uint8 the equivalent of ReLU. Therefore, one goal of the quantized training
process is to learn how to use the entire range of uint8 (i.e, 0 to 255) so the
activation function fit perfectly with the saturation cast.

The training process is as follows: The network model is as before and weights
are in float. But we added the quantization layer after each layer to *simulate
quantization effect* in the forward-pass while the backpropagation is as usual.
The quantization layer is to introduce the rounding behavior for the training.

After the training, we can simply make

\[\begin{aligned} \text{clamp}(r; a,b) &= \min(\max(x,a), b) \\ s(a,b,n) &= \frac{b-a}{n-1} \\ q(r; a,b,n) &= \big\lfloor\frac{\text{clamp}(r;a,b)}{s(a,b,n)}\rceil s(a,b,n) + a \end{aligned}\]and set \(a=\min w\) and \(b=\max w\) for weights \(w\) (but do some nudge to make quantization use only \(-127\) to \(+127\) and make zero exactly representable). It would be less trivial for the activation function and we need some input samples from the training data to observe the actual range of activation.

## Bibliographic data

```
@inproceedings{
title = "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference",
author = "Benoit Jacob and Skirmantas Kligys and Bo Chen and Menglong Zhu and Matthew Tang and Andrew Howard and Hartwig Adam and Dmitry Kalenichenko",
booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
pages = "2704--2713",
year = "2018",
arxiv = "1712.05877",
url = "https://openaccess.thecvf.com/content_cvpr_2018/html/Jacob_Quantization_and_Training_CVPR_2018_paper.html",
}
```