TY - JOUR
T1 - Neuromemristive Circuits for Edge Computing: A Review
AU - Krestinskaya, Olga
AU - James, Alex Pappachen
AU - Chua, Leon Ong
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
AB - The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
UR - https://ieeexplore.ieee.org/document/8667457/
UR - http://www.scopus.com/inward/record.url?scp=85077664697&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2019.2899262
DO - 10.1109/TNNLS.2019.2899262
M3 - Article
SN - 2162-2388
VL - 31
SP - 4
EP - 23
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 1
ER -