Neuromorphic Computing Devices and Systems Enabled by Two-Dimensional Materials
- Submission status
- Open
- Submission deadline
Neuromorphic computing is a cutting-edge computational approach that draws inspiration from the architecture and functioning of the human brain, but it requires the materials or hardware to emulate the brain's innate efficiency, adaptability, and parallelism for unlocking its full transformative potential. Two-dimensional materials are a new class of materials that are atomically thin but possess intricate properties such as exceptional electrical conductivity, quantum tenability and energy efficiency. Leveraging these properties holds immense potential for advancing neuromorphic computing, as they enable the creation of ultra-efficient synaptic connections, rapid information processing through quantum effects, and efficient signal conversion and modulation in confined two-dimensional space. This convergence of two-dimensional materials and neuromorphic computing opens avenues for developing brain-inspired computing systems that are not only energy-efficient and highly parallel but also capable of emulating the brain's remarkable adaptability and cognitive prowess.
In this collection, we aim to build on the existing knowledge and further drive the development of multi-functional and highly integrated devices/systems and their applications. The topics include, but are not limited to:
1) Bioinspired synapses and neurons nanodevices/ systems:
- Novel mechanisms of bioinspired devices (e.g., new materials, new device structures)
- Applications of bioinspired devices/systems in life sciences
- Bioinspired devices/systems for biomimetic applications
- A combination of bioinspired devices/ systems and advanced algorithms
2) 2D material memories for hardware acceleration of neural network algorithms:
- High-throughput demonstration of multiply-accumulate operations.
- Realization of activation functions (e.g., ReLU, Softmax)
- System demonstration based on spiking input.
- System demonstration based on physical information input (e.g., optics, mechanics)
3) All-in-one perception, memory, and computing devices/ systems:
- Novel mechanisms of all-in-one devices
- Novel systems based on all-in-one devices.
Editors
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Xinran Wang, PhD
Nanjing University, China.
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Peng Zhou, PhD
Fudan University, China.
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Mario Lanza, PhD
PhD, Associate Professor, King Abdullah University of Science and Technology, Saudi Arabi
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Saptarshi Das, PhD
Pennsylvania State University, USA
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Chunsen Liu, PhD
Fudan University, China.
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Zhihao Yu, PhD
Nanjing University of Posts and Telecommunications, China
