Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Learning Framework for n-bit Quantized Neural Networks toward FPGAs   The quantized neural network (QNN) is an efficient approach for network
compression and can be widely used in the implementation of FPGAs. This paper
proposes a novel learning framework for n-bit QNNs, whose weights are
constrained to the power of two. To solve the gradient vanishing problem, we
propose a reconstructed gradient function for QNNs in back-propagation
algorithm that can directly get the real gradient rather than estimating an
approximate gradient of the expected loss. We also propose a novel QNN
structure named n-BQ-NN, which uses shift operation to replace the multiply
operation and is more suitable for the inference on FPGAs. Furthermore, we also
design a shift vector processing element (SVPE) array to replace all 16-bit
multiplications with SHIFT operations in convolution operation on FPGAs. We
also carry out comparable experiments to evaluate our framework. The
experimental results show that the quantized models of ResNet, DenseNet and
AlexNet through our learning framework can achieve almost the same accuracies
with the original full-precision models. Moreover, when using our learning
framework to train our n-BQ-NN from scratch, it can achieve state-of-the-art
results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102
platform show that our n-BQ-NN with our SVPE can execute 2.9 times faster than
with the vector processing element (VPE) in inference. As the SHIFT operation
in our SVPE array will not consume Digital Signal Processings (DSPs) resources
on FPGAs, the experiments have shown that the use of SVPE array also reduces
average energy consumption to 68.7% of the VPE array with 16-bit.

7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph