Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Learning Inference Scheme Based on Pipelined Matrix
  Multiplication Acceleration Design and Non-uniform Quantization   Matrix multiplication is the bedrock in Deep Learning inference application.
When it comes to hardware acceleration on edge computing devices, matrix
multiplication often takes up a great majority of the time. To achieve better
performance in edge computing, we introduce a low-power Multi-layer Perceptron
(MLP) accelerator based on a pipelined matrix multiplication scheme and a
nonuniform quantization methodology. The implementation is running on
Field-programmable Gate Array (FPGA) devices and tested its performance on
handwritten digit classification and Q-learning tasks. Results show that our
method can achieve better performance with fewer power consumption.

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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph