TY - JOUR
T1 - Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends
AU - Lakshmi Varshika, M.
AU - Corradi, Federico
AU - Das, Anup Kumar
PY - 2022/5/18
Y1 - 2022/5/18
N2 - 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.
AB - 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.
KW - nonvolatile memory
KW - spiking neural networks
KW - neuromorphic computing
KW - spiking neural network (SNN)
UR - http://www.scopus.com/inward/record.url?scp=85130118319&partnerID=8YFLogxK
U2 - 10.3390/electronics11101610
DO - 10.3390/electronics11101610
M3 - Article
SN - 2079-9292
VL - 11
JO - Electronics
JF - Electronics
IS - 10
M1 - 1610
ER -