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.

翻译：Transformer推理需要高计算精度，而模拟存算一体（CIM）因自身计算误差难以实现这一目标。为解决该挑战，我们提出电容重构存算一体（CR-CIM）方案，通过10位模数转换器（ADC）实现高计算精度的模拟CIM，同时具备高面积/能效比。CR-CIM通过重构电容阵列实现计算与ADC转换双功能，显著节省芯片面积。此外，CR-CIM在运算过程中保持信号电荷静止以消除信号衰减，使比较器能效提升4倍。我们进一步提出软件-模拟协同设计技术，将多数投票机制集成至10位ADC中，根据运行层动态优化CIM噪声性能以降低推理功耗。CR-CIM在模拟CIM中实现了最高计算精度，其818 TOPS/W的能效与现有最优方案相当。考虑SQNR和CSNR的综合品质因数（FoM）较先前成果分别提升2.3倍和1.5倍。基于Vision Transformer（ViT）的推理实验表明，该设计在CIFAR10数据集上达到95.8%的最高准确率（面向模拟CIM）。