TY - JOUR
T1 - Tutorial on memristor-based computing for smart edge applications
AU - Gebregiorgis, Anteneh
AU - Singh, Abhairaj
AU - Yousefzadeh, Amirreza
AU - Wouters, Dirk
AU - Bishnoi, Rajendra
AU - Catthoor, Francky
AU - Hamdioui, Said
PY - 2023
Y1 - 2023
N2 - Smart computing on edge-devices has demonstrated huge potential for various application sectors such as personalized healthcare and smart robotics. These devices aim at bringing smart computing close to the source where the data is generated or stored, while coping with the stringent resource budget of the edge platforms. The conventional Von-Neumann architecture fails to meet these requirements due to various limitations e.g., the memory-processor data transfer bottleneck. Memristor-based Computation-In-Memory (CIM) has the potential to realize such smart edge computing for data-dominated Artificial Intelligence (AI) applications by exploiting both the inherent properties of the architecture and the physical characteristics of the memristors. This paper discusses different aspects of CIM, including classification, working principle, CIM potentials and CIM design-flow. The design-flow is illustrated through two case studies to demonstrate the huge potential of CIM in realizing orders of magnitude improvement in energy-efficiency as compared to the conventional architectures. Finally future challenges and research directions of CIM are covered.
AB - Smart computing on edge-devices has demonstrated huge potential for various application sectors such as personalized healthcare and smart robotics. These devices aim at bringing smart computing close to the source where the data is generated or stored, while coping with the stringent resource budget of the edge platforms. The conventional Von-Neumann architecture fails to meet these requirements due to various limitations e.g., the memory-processor data transfer bottleneck. Memristor-based Computation-In-Memory (CIM) has the potential to realize such smart edge computing for data-dominated Artificial Intelligence (AI) applications by exploiting both the inherent properties of the architecture and the physical characteristics of the memristors. This paper discusses different aspects of CIM, including classification, working principle, CIM potentials and CIM design-flow. The design-flow is illustrated through two case studies to demonstrate the huge potential of CIM in realizing orders of magnitude improvement in energy-efficiency as compared to the conventional architectures. Finally future challenges and research directions of CIM are covered.
KW - Computation-In-Memory
KW - Edge-AI
KW - Memristor
UR - http://www.scopus.com/inward/record.url?scp=85153689746&partnerID=8YFLogxK
U2 - 10.1016/j.memori.2023.100025
DO - 10.1016/j.memori.2023.100025
M3 - Article
AN - SCOPUS:85153689746
SN - 2773-0646
VL - 4
JO - Memories - Materials, Devices, Circuits and Systems
JF - Memories - Materials, Devices, Circuits and Systems
M1 - 100025
ER -
