Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision   Model size and inference speed/power have become a major challenge in the
deployment of Neural Networks for many applications. A promising approach to
address these problems is quantization. However, uniformly quantizing a model
to ultra low precision leads to significant accuracy degradation. A novel
solution for this is to use mixed-precision quantization, as some parts of the
network may allow lower precision as compared to other layers. However, there
is no systematic way to determine the precision of different layers. A brute
force approach is not feasible for deep networks, as the search space for
mixed-precision is exponential in the number of layers. Another challenge is a
similar factorial complexity for determining block-wise fine-tuning order when
quantizing the model to a target precision. Here, we introduce Hessian AWare
Quantization (HAWQ), a novel second-order quantization method to address these
problems. HAWQ allows for the automatic selection of the relative quantization
precision of each layer, based on the layer's Hessian spectrum. Moreover, HAWQ
provides a deterministic fine-tuning order for quantizing layers, based on
second-order information. We show the results of our method on Cifar-10 using
ResNet20, and on ImageNet using Inception-V3, ResNet50 and SqueezeNext models.
Comparing HAWQ with state-of-the-art shows that we can achieve similar/better
accuracy with $8\times$ activation compression ratio on ResNet20, as compared
to DNAS~\cite{wu2018mixed}, and up to $1\%$ higher accuracy with up to $14\%$
smaller models on ResNet50 and Inception-V3, compared to recently proposed
methods of RVQuant~\cite{park2018value} and HAQ~\cite{wang2018haq}.
Furthermore, we show that we can quantize SqueezeNext to just 1MB model size
while achieving above $68\%$ top1 accuracy on ImageNet.

25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg