The rapidly evolving research field of brain-inspired (neuromorphic) computing aims to address modern computing challenges, such as the memory wall problem (the limited rate of data transfer between CPU and memory), for data intensive applications in the era of artificial intelligence and internet-of-things. Neuromorphic computing has attracted strong interests due to its massive parallelism, high energy efficiency, and cognitive functions. Different types of emerging technology solutions, such as the charge-based and resistance-based memory devices, provide unique functional behaviors for emulating artificial synapses and neurons, and offer distinct design opportunities for implementing in-memory neuromorphic computing systems at circuit and architecture levels.
The research topic aims to present the recent advances of cutting-edge research and developments in emerging memory technologies with applications in brain-inspired neuromorphic computing. The research topic focuses on the functional potential of diverse memory devices to emulate various functions of biological synapses and neurons for in-memory computing. More specifically, this research topic’s interests include the simulation or experimental implementation of different brain-inspired primitives, circuits, computing systems, and neural networks. The innovations of advanced memory devices for neuromorphic applications are also within the scope of the research topic. Exploring the highly efficient realizations of artificial neurons and synapses, and their integration at the circuit and architecture levels is critical to the development of highly efficient future neuromorphic systems.
We invite manuscripts reporting novel research results (experimental, numerical, and theoretical) on neuromorphic computing with emerging memory devices. To this purpose, we welcome articles addressing (but not limited to) the following:
- Novel fabrication techniques for memory devices to facilitate neuromorphic system implementation, insights on variability and endurance limits of memory devices for neuromorphic circuits/systems and new insights on the design challenges in memory device based neuromorphic systems
- Design of artificial synaptic and neural primitives utilizing charge-based memory devices and resistance-based memory devices, for example, Resistive Random-Access Memory (ReRAM), Phase Change Memory (PCM), Spin Transfer Torque Magneto Resistive Random-Access Memory (STT-MRAM), Ferroelectric Field Effect Transistor (FeFET), Ferroelectric Tunnel Junction (FTJ), etc.
- Novel computing systems leveraging emerging memory device properties and device-circuit-architecture-algorithm co-design with emerging memory technologies.
- We also solicit reviews of hardware-related neuromorphic implementations, perspectives on the nature, past, present, and future of neuromorphic implementations and constructive commentaries on specific papers or pieces of work.
The rapidly evolving research field of brain-inspired (neuromorphic) computing aims to address modern computing challenges, such as the memory wall problem (the limited rate of data transfer between CPU and memory), for data intensive applications in the era of artificial intelligence and internet-of-things. Neuromorphic computing has attracted strong interests due to its massive parallelism, high energy efficiency, and cognitive functions. Different types of emerging technology solutions, such as the charge-based and resistance-based memory devices, provide unique functional behaviors for emulating artificial synapses and neurons, and offer distinct design opportunities for implementing in-memory neuromorphic computing systems at circuit and architecture levels.
The research topic aims to present the recent advances of cutting-edge research and developments in emerging memory technologies with applications in brain-inspired neuromorphic computing. The research topic focuses on the functional potential of diverse memory devices to emulate various functions of biological synapses and neurons for in-memory computing. More specifically, this research topic’s interests include the simulation or experimental implementation of different brain-inspired primitives, circuits, computing systems, and neural networks. The innovations of advanced memory devices for neuromorphic applications are also within the scope of the research topic. Exploring the highly efficient realizations of artificial neurons and synapses, and their integration at the circuit and architecture levels is critical to the development of highly efficient future neuromorphic systems.
We invite manuscripts reporting novel research results (experimental, numerical, and theoretical) on neuromorphic computing with emerging memory devices. To this purpose, we welcome articles addressing (but not limited to) the following:
- Novel fabrication techniques for memory devices to facilitate neuromorphic system implementation, insights on variability and endurance limits of memory devices for neuromorphic circuits/systems and new insights on the design challenges in memory device based neuromorphic systems
- Design of artificial synaptic and neural primitives utilizing charge-based memory devices and resistance-based memory devices, for example, Resistive Random-Access Memory (ReRAM), Phase Change Memory (PCM), Spin Transfer Torque Magneto Resistive Random-Access Memory (STT-MRAM), Ferroelectric Field Effect Transistor (FeFET), Ferroelectric Tunnel Junction (FTJ), etc.
- Novel computing systems leveraging emerging memory device properties and device-circuit-architecture-algorithm co-design with emerging memory technologies.
- We also solicit reviews of hardware-related neuromorphic implementations, perspectives on the nature, past, present, and future of neuromorphic implementations and constructive commentaries on specific papers or pieces of work.
