Artificial neural networks (ANNs) are widely used in numerous artificial intelligence-based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN for the artificial intelligence of things (AIoT) and power-constrained device applications. Therefore, among various ANN algorithms, quantized neural networks (QNNs) have garnered considerable attention because they require fewer computational resources with minimal energy consumption. Herein, an oxide-based ternary charge-trap transistor (CTT) that provides three discrete states and non-volatile memory characteristics are introduced, which are desirable for QNN computing. By employing a differential pair of ternary CTTs, an artificial synaptic segregation with multilevel quantized values for QNNs is demostrated. The approach establishes a platform that combines the advantages of multiple states and robustness to noise for in-memory computing to achieve reliable QNN performance in hardware, thereby facilitating the development of energy-efficient AIoT.
CITATION STYLE
Baek, Y., Bae, B., Yang, J., Lee, D., Lee, H. S., Park, M., … Lee, K. (2023). Quantized Neural Network via Synaptic Segregation Based on Ternary Charge-Trap Transistors. Advanced Electronic Materials, 9(11). https://doi.org/10.1002/aelm.202300303
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