Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method

Crossbar-enabled analog computing-in-memory (CACIM) systems can significantly improve the computation speed and energy efficiency of deep neural networks (DNNs). However, the transition of DNN from the digital system to CACIM system usually reduces its accuracy. The major issue is that the weights of DNN are stored and calculated directly on analog quantities in CACIM system. The variation and programming overhead of the analog weight limit the precision. Therefore, a suitable quantization algorithm is important when deploying a DNN into CACIM systems to obtain less accuracy loss. The analog weight has its unique advantages when doing quantization. Because there is no encoding and decoding process, the range of quantization function will not affect the computing process. Therefore, a generalized quantization method which does not constrain the range of quanta and can obtain less quantization error will be effective in CACIM system. For the first time, we introduced a generalized quantization method into CACIM systems and showed the superior performance on a series of computer vision tasks, such as image classification, object detection, and semantic segmentation. Using the generalized quantization method, the DNN with 8-level analog weights can outperform the 32-bit networks. With fewer levels, the generalized quantization method can obtain less accuracy loss than other uniform quantization methods.