Leveraging spintronic devices for efficient approximate logic and stochastic neural networks

Shaahin Angizi, Zhezhi He, Yu Bai, Jie Han, Mingjie Lin, Ronald F. DeMara, Deliang Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


ITRS has identified nano-magnet based spintronic devices as promising post-CMOS technologies for information processing and data storage due to their ultra-low switching energy, non-volatility, superior endurance, excellent retention time, high integration density and compatibility with CMOS technology. As for data storage, spintronic memory has been widely accepted as a universal high performance next-generation non-volatile memory candidate. As for information processing, spintronic computing remains complementary in its features to CMOS technology. In this paper, we present two innovative spintronic computing primitives, i.e. spintronic approximate logic and spintronic stochastic neural network, which both leverage the intrinsic spintronic device physics to achieve much more compact and efficient designs than CMOS counterparts. In spintronic approximate logic, we employ the intrinsic current-mode thresholding operation to implement an accuracy-configurable adder and further demonstrate its application in approximate DSP applications. In spintronic stochastic neural networks, we leverage the stochastic properties of domain wall devices and magnetic tunnel junction to implement a low-power and robust artificial neural network design.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2018 - Proceedings of the 2018 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450357241
StatePublished - May 30 2018
Externally publishedYes
Event28th Great Lakes Symposium on VLSI, GLSVLSI 2018 - Chicago, United States
Duration: May 23 2018May 25 2018

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference28th Great Lakes Symposium on VLSI, GLSVLSI 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Engineering


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