Security Opportunities in Nano Devices and Emerging Technologies
Nanoscale memory architectures for neuromorphic computing
Publication
, Chapter
Yang, C; Li, H; Chen, Y
January 1, 2017
216On one hand, machine learning has been widely used in data processing to help users understand the underlying property of the data [1]. As a popular type of machine learning model, neural network [2] processes input data by multiplying them with layers of weighted connections. Many embedded hardware engines, including field-programmable gate array (FPGA) and system-on-chip (SoC), have been developed to implement neural networks with high speed and efficiency, for example, Qualcomm's cognitive computing platform [3].
Duke Scholars
DOI
ISBN
9781138035775
Publication Date
January 1, 2017
Start / End Page
215 / 234
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Yang, C., Li, H., & Chen, Y. (2017). Nanoscale memory architectures for neuromorphic computing. In Security Opportunities in Nano Devices and Emerging Technologies (pp. 215–234). https://doi.org/10.1201/9781315265056
Yang, C., H. Li, and Y. Chen. “Nanoscale memory architectures for neuromorphic computing.” In Security Opportunities in Nano Devices and Emerging Technologies, 215–34, 2017. https://doi.org/10.1201/9781315265056.
Yang C, Li H, Chen Y. Nanoscale memory architectures for neuromorphic computing. In: Security Opportunities in Nano Devices and Emerging Technologies. 2017. p. 215–34.
Yang, C., et al. “Nanoscale memory architectures for neuromorphic computing.” Security Opportunities in Nano Devices and Emerging Technologies, 2017, pp. 215–34. Scopus, doi:10.1201/9781315265056.
Yang C, Li H, Chen Y. Nanoscale memory architectures for neuromorphic computing. Security Opportunities in Nano Devices and Emerging Technologies. 2017. p. 215–234.
DOI
ISBN
9781138035775
Publication Date
January 1, 2017
Start / End Page
215 / 234