Compute-in-memory (CIM) architecture is an effective way to reduce the energy efficiency of convolutional neural networks (DNNs). This letter presents a time-domain compute-in-memory design based on pulse width modulation. Unique-weight convolution method is proposed for multi-bits convolutional operations. A time-charge domain quantizer is also presented to quantify the computation pulses of multi-rows in parallel. Fabricated in 28nm CMOS technology, this design achieves 56.9% and 67.3% accuracy, 59.84Tops/W and 67.45Tops/W energy efficiency for 1-8-b AlexNet and VGG16, respectively.