Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Reconfigurable neuromorphic components and algorithms for next-generation artificial intelligence

Technical Report ·
DOI:https://doi.org/10.2172/2588895· OSTI ID:2588895
 [1];  [1];  [1];  [2];  [3];  [3];  [3];  [4];  [4];  [4];  [5];  [1];  [1];  [6];  [1];  [3];  [3];  [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Stanford Univ., CA (United States)
  3. Texas A & M Univ., College Station, TX (United States)
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  5. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  6. Univ. of Michigan, Ann Arbor, MI (United States)
+ Show Author Affiliations
Digital transistor-based general-purpose hardware (e.g., central processing units) is the dominant solution to support both traditional computing (logic, arithmetic, etc.) as well as modern artificial intelligence. State-of-the-art research has shown feasibility of post-digital physics-based neuromorphic hardware, which is hypothesized to support artificial intelligence algorithms with orders-of-magnitude improved time/energy efficiencies. But such research has not been widely deployed mainly because of such novel hardware’s extreme application-specificity, and the dominance of low-cost general-purpose (but inefficient) digital hardware. To make use of the novel algorithms and the superlative performance of physics-based hardware, we need to identify scientific principles that can enable generality in physics-based hardware. This work resulted in two important broad outcomes – first, we demonstrate fully reconfigurable neuromorphic components, and second, we demonstrate a viable artificial intelligence learning algorithm that can exploit the functioning of neuromorphic hardware. We demonstrate up to five orders of magnitude improvement in energy efficiency compared to the best general-purpose digital hardware.
Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
DOE Contract Number:
NA0003525
OSTI ID:
2588895
Report Number(s):
SAND--2024-12441; 1757539
Country of Publication:
United States
Language:
English

Similar Records

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators
Conference · Wed Dec 22 23:00:00 EST 2021 · OSTI ID:1845403
Intrinsically stretchable neuromorphic devices for on-body processing of health data with artificial intelligence
Journal Article · Wed Aug 03 20:00:00 EDT 2022 · Matter (Online) · OSTI ID:1909343
Reconfigurable perovskite nickelate electronics for artificial intelligence
Journal Article · Wed Feb 02 19:00:00 EST 2022 · Science · OSTI ID:1898604

Related Subjects

97 MATHEMATICS AND COMPUTING