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Low-Dimensional Nanoelectronic Materials for Energy-Efficient Edge Computing
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AI
DOI：10.1149/2.F13254IF.png)
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Abstract
En 中文
The rapid deployment of AI agents in IoT, healthcare, robotics, and consumer electronics, combined with the stalled scaling and limited energy efficiency of silicon CMOS computing architectures, have placed unsustainable demands on electricity and water use in data centers. These problems are exacerbated for edge computing applications that require on-chip learning in evolving environments. Recent research in 1D and 2D nanomaterials exemplifies co-design approaches to discover and optimize new materials, devices, circuit architectures, and algorithms. These low-dimensional nanomaterials have shown promise for the realization of high-performance digital electronics and bioinspired approaches for energy-efficient edge AI. This work is fueled by a growing library of low-dimensional nanoelectronic materials, 3D integration, and vdW heterojunctions, resulting in simplified and reconfigurable neuromorphic circuits.
Keywords:
edge computing
nanoelectronic materials
sustainability
Journal
IF:
1.4
Papers:
57
Citations:
1.0K