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Neuro-inspired computing with emerging memories: where device physics meets learning algorithms
Author(s): Haitong Li; Priyanka Raina; H.-S. Philip Wong

Paper Abstract

Modern cognitive computing workloads require computing systems tailored to the applications, where the underlying hardware fabrics should naturally match the characteristics of learning algorithms and compute kernels. With emerging memory technologies (e.g., resistive RAM (RRAM), magnetic RAM (MRAM)), we design neuro-inspired computing systems that exploit technology characteristics such as rich device physics, circuit architecture, and integration capabilities with CMOS and beyond-CMOS technologies. Our methodology is built upon a combination of experimental characterization, cross-stack modeling, and system integration, illustrated by case studies for neural networks and highdimensional (HD) computing. Finally, we discuss the prospects of heterogeneous learning machines that emphasize the integration of compute kernels and learning algorithms, as well as the integration of emerging nanotechnologies.

Paper Details

Date Published: 10 September 2019
PDF: 7 pages
Proc. SPIE 11090, Spintronics XII, 110903L (10 September 2019); doi: 10.1117/12.2529916
Show Author Affiliations
Haitong Li, Stanford Univ. (United States)
Priyanka Raina, Stanford Univ. (United States)
H.-S. Philip Wong, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 11090:
Spintronics XII
Henri-Jean M. Drouhin; Jean-Eric Wegrowe; Manijeh Razeghi, Editor(s)

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