A Reconfigurable micro-Processing Element for Mixed Precision CNNs

Mixed-precision quantization can compress the size of the Convolution Neural Networks (CNNs) without reducing the accuracy of the network. A fixed bit width CNN accelerator needs to match the largest bit width in mixed- precision CNNs, which cause a huge waste of resource. In order to match the multiple quantization bit width of mixed- precision CNNs, we propose a reconfigurable microprocessing element (RmPE) that supports multi-precision parallel multiplication and addition operations. After testing, when the CNN accelerator with RmPE infers mixed-precision VGG-16 and ResNet-50 on the Ultra96-V2 platform, the computing performance of the accelerator reaches 216.6 GOPS and 214 GOPS, and the computing efficiency of the accelerator reaches 0.63 GOPS/DSP and 0.64 GOPS/DSP. Compared with the fixed-bit width CNN accelerator, our accelerator has higher computational efficiency.