Learning in AI Processor


  • Xinhua Wang Communication software and ASIC design NERC, The 54th Research Institute CETC, China
  • Weikang Wu Communication software and ASIC design NERC, The 54th Research Institute CETC, China




AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.


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[2] Yu-Hsin Chen;Joel Emer;Vivienne Sze. " Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks," in Computer Architecture (ISCA), 2016.

[3] Zidong Du et al. "ShiDianNao: Shifting vision processing closer to the sensor," in Computer Architecture (ISCA), 2015.

[4] K.He, X.Zhang, S.Ren, and J.Sun. "Deep Residual Learning for lmage Recognition,"in IEEE CVPR, 2016.

[5] J.Cong and B.Xiao. “Minimizing computation in convolutional neural networks." in ICANN, 2014.


How to Cite

Wang, X., & Wu, W. (2021). Learning in AI Processor. Artificial Intelligence Advances, 3(2), 71–72. https://doi.org/10.30564/aia.v3i2.3878


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