Neuro-inspired computing with emerging memories: where device physics meets learning algorithms

Haitong Li; Priyanka Raina; H.-S. Philip Wong

doi:10.1117/12.2529916

16 September 2019 Neuro-inspired computing with emerging memories: where device physics meets learning algorithms

Haitong Li, Priyanka Raina, H.-S. Philip Wong

Proceedings Volume 11090, Spintronics XII; 110903L (2019) https://doi.org/10.1117/12.2529916
Event: SPIE Nanoscience + Engineering, 2019, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Haitong Li, Priyanka Raina, and H.-S. Philip Wong "Neuro-inspired computing with emerging memories: where device physics meets learning algorithms", Proc. SPIE 11090, Spintronics XII, 110903L (16 September 2019); https://doi.org/10.1117/12.2529916

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available