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

Abstract

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.