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Title: Iteratively Training Look-Up Tables for Network Quantization

Paper content:
Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements.
In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values.
We show that this method is very flexible and that many other techniques can be seen as special cases of LUT-Q.
For example, we can constrain the dictionary trained with LUT-Q to generate networks with pruned weight matrices or restrict the dictionary to powers-of-two to avoid the need for multiplications.
In order to obtain fully multiplier-less networks, we also introduce a multiplier-less version of batch normalization.
Extensive experiments on image recognition and object detection tasks show that LUT-Q consistently achieves better performance than other methods with the same quantization bitwidth.
In this paper, we propose a training method for reducing the size and the number of operations of a deep neural network (DNN) that we call look-up table quantization (LUT-Q).
As depicted in Fig. 1 , LUT-Q trains a network that represents the weights W ∈ R O×I of one layer by a dictionary d ∈ R K and assignments A ∈ [1, . . . , K] O×I such that Q oi = d Aoi , i.e., elements of Q are restricted to the K dictionary values in d.
To learn the assignment matrix A and dictionary d, we iteratively update them after each minibatch.
Our LUT-Q algorithm, run for each mini-batch, is summarized in TAB1 LUT-Q has the advantage to be very flexible.
By simple modifications of the dictionary d or the assignment matrix A, it can implement many weight compression schemes from the literature.
For example, we can constrain the assignment matrix and the dictionary in order to generate a network with pruned weight matrices.
Alternatively, we can constrain the dictionary to contain only the values {−1, 1} and obtain a Binary Connect Network BID3 , or to {−1, 0, 1} resulting in a Ternary Weight Network BID12 .
Furthermore, with LUT-Q we can also achieve Multiplier-less networks by either choosing a dictionary d whose elements d k are of the form d k ∈ {±2 b k } for all k = 1, . . . , K with b k ∈ Z, or by rounding the output of the k-means algorithm to powers-of-two.
In this way we can learn networks whose weights are powers-of-two and can, hence, be implemented without multipliers.The memory used for the parameters is dominated by the weights in affine/convolution layers.
Using LUT-Q, instead of storing W, the dictionary d and the assignment matrix A are stored.
Hence, for an affine/convolution layer with N parameters, we reduce the memory usage in bits from N B float to just KB float + N ⌈log 2 K⌉, where B float is the number of bits used to store one weight.
Furthermore, using LUT-Q we also achieve a reduction in the number of computations: for example, affine layers trained using LUT-Q need to compute just K multiplications at inference time, instead of I multiplications for a standard affine layer with I input nodes.
DISPLAYFORM0
We have presented look-up table quantization, a novel approach for the reduction of size and computations of deep neural networks.
After each minibatch update, the quantization values and assignments are updated by a clustering step.
We show that the LUT-Q approach can be efficiently used for pruning weight matrices and training multiplier-less networks as well.
We also introduce a new form of batch normalization that avoids the need for multiplications during inference.As argued in this paper, if weights are quantized to very low bitwidth, the activations may dominate the memory footprint of the network during inference.
Therefore, we perform our experiments with activations quantized uniformly to 8-bit.
We believe that a non-uniform activation quantization, where the quantization values are learned parameters, will help quantize activations to lower precision.
This is one of the promising directions for continuing this work.Recently, several papers have shown the benefits of training quantized networks using a distillation strategy BID8 BID14 .
Distillation is compatible with our training approach and we are planning to investigate LUT-Q training together with distillation.

assistant: text: In this paper we introduce a training method, called look-up table quantization (LUT-Q), which learns a dictionary and assigns each weight to one of the dictionary's values