Quantized Neural Network via Synaptic Segregation Based on Ternary Charge-Trap Transistors

초록

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. An oxide-based ternary charge-trap transistor (CTT) offers three distinct states and non-volatile memory characteristics. The ternary CTT exhibits robustness and reliability under analog signal variation, enabling effective pulse modulation for iterative programming and erasing operations. The quantized neural network based on ternary CTTs achieves high classification accuracy for image datasets, demonstrating its potential for hardware-based artificial intelligence computing.image

키워드