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Position: Home > issue > May 10, 2022 Volume 3, Issue 4
Accelerating deep learning with high energy efficiency: From microchip to physical systems
Category:   Commentary   Download:  PDF  Figure  Endnote
Author: Huanhao Li, Zhipeng Yu, Qi Zhao, Tianting Zhong, Puxiang Lai

c2.jpg

Diagram of deep learning realizations


In the era of digits and internet, massive data have been continuously generated from a variety of sources, including video, photo, audio, text, internet of things, etc. It is intuitive that more accurate patterns can be obtained by feeding more data for effective analysis; despite the data redundancy, a clearer picture can be delineated for better decision-making. However, traditional methods, even in machine learning, do not benefit from the expanding amount of data, whose performance nearly saturates when the data collection is large enough.





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