  Transformer inference requires high compute accuracy; achieving this using
analog CIMs has been difficult due to inherent computational errors. To
overcome this challenge, we propose a Capacitor-Reconfiguring CIM (CR-CIM) to
realize high compute accuracy analog CIM with a 10-bit ADC attaining
high-area/power efficiency. CR-CIM reconfigures its capacitor array to serve
dual purposes: for computation and ADC conversion, achieving significant area
savings. Furthermore, CR-CIMs eliminate signal attenuation by keeping the
signal charge stationary during operation, leading to a 4x improvement in
comparator energy efficiency. We also propose a software-analog co-design
technique integrating majority voting into the 10-bit ADC to dynamically
optimize the CIM noise performance based on the running layer to further save
inference power. Our CR-CIM achieves the highest compute-accuracy for analog
CIMs, and the power efficiency of 818 TOPS/W is competitive with the
state-of-the-art. Furthermore, the FoM considering SQNR and CSNR is 2.3x and
1.5x better than previous works, respectively. Vision Transformer (ViT)
inference is achieved and realizes a highest CIFAR10 accuracy of 95.8% for
analog CIMs.
