Conclusion: This paper presents a deep separable convolutional
neural network accelerator designed specifically for ShuffleNetV2. Based
on the features of ShuffleNetV2, optimizations are made to the network
structure, achieving a 1.09% increase in accuracy while reducing the
parameters by 0.18M. The paper also proposes a reconfigurable hardware
accelerator that supports both PwC and DwC. The power consumption of
this accelerator is only 7.3W while achieving a power efficiency of
13.45 GOPS/W. The running frame rate achieves 675.7 fps.
Acknowledgments: The authors thank to the support by the Science
and Technology Program of Guangdong Province under Grant
2022B0701180001.
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