Binarized neural networks: a path toward robust low-power AI

Damien Querlioz

doi:10.1117/12.3028824

4 October 2024 Binarized neural networks: a path toward robust low-power AI

Damien Querlioz

Proceedings Volume PC13119, Spintronics XVII; PC131190R (2024) https://doi.org/10.1117/12.3028824
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

In the field of in-memory computing for artificial intelligence (AI), the traditional reliance on analog memory for storing synaptic weights presents significant challenges, particularly when using Magnetic Tunnel Junctions (MTJs), which are limited by their inherently binary nature. However, the advent of Binarized Neural Networks (BNNs) has opened a new avenue for leveraging binary storage mechanisms, offering a promising solution for energy-constrained and miniaturized AI systems. This presentation unveils a fully integrated implementation of a BNN using 32k binary memristors. We detail the design and fabrication of our system and its operational excellence even under variable power conditions in an energy harvesting scenario, demonstrating its potential as a resilient and energy-frugal AI platform. While our current work employs hafnium oxide memristors, the design principles and architecture we propose are readily adaptable to MTJs, suggesting a seamless transition pathway to spintronic implementations in the future. We also compare our approach to other research spintronic research endeavors, to prove a comprehensive view of the field's direction.

Conference Presentation

Citation Download Citation

Damien Querlioz "Binarized neural networks: a path toward robust low-power AI", Proc. SPIE PC13119, Spintronics XVII, PC131190R (4 October 2024); https://doi.org/10.1117/12.3028824

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