This talk introduces the possibility of charge-domain computing using a 1FeFET-1Capacitance (1F1C) macro based on a 2-bit ferroelectric field-effect transistor (FeFET). This cell operating in the charge domain is marking a significant advancement for compute-in-memory (CIM) which improves the energy efficiency but also robustness due to the low capacitor mismatch. Traditionally, NVMs, such as FeFETs or resistive RAMs (RRAMs), have operated in a single-bit fashion, limiting their computational density and throughput. In contrast, the proposed 2-bit FeFET cell enables higher storage density and improves the computational efficiency in CIM architectures. The macro achieves 111.6 TOPS/W, highlighting its energy efficiency, and demonstrates robust performance on the CIFAR-10 dataset, achieving 89% accuracy with a VGG-8 neural network. These findings underscore the potential of charge-domain, multilevel NVM cells in pushing the boundaries of artificial intelligence (AI) acceleration and energy-efficient computing.