Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends
A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.
- Research Organization:
- Drexel Univ., Philadelphia, PA (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); USDOE; USDOE Office of Science (SC)
- Grant/Contract Number:
- SC0022014
- OSTI ID:
- 1868397
- Alternate ID(s):
- OSTI ID: 1981123
- Journal Information:
- Electronics, Journal Name: Electronics Journal Issue: 10 Vol. 11; ISSN ELECGJ; ISSN 2079-9292
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- Switzerland
- Language:
- English
Similar Records
Probabilistic Neural Computing with Stochastic Devices
Journal Article
·
Wed Nov 16 19:00:00 EST 2022
· Advanced Materials
·
OSTI ID:1898714