FPGA-Friendly Architecture of Processing Elements For Efficient and Accurate Quantized CNNs

An FPGA-friendly processing element based on the small logarithmic floating-point (SLFP) format is proposed. The proposed processing elements not only support inner product but also perform various nonlinear activation functions (NAF), which consume 674× LUT6s and 7× DSPs and operate at 450MHz in a pipeline manner for Zynq-7000. In addition, as the distribution of SLFP numbers is not uniform, this brief revises the weight decay scheme in the quantization aware training process to explore the optimum quantized weights. Compared with INT8 based design, the proposed method balances the resource usage between lookup tables and digital signal processing blocks. The accuracy loss of the quantized model based on the 8-bit SLFP is also small due to the high dynamic range of SLFP format. Moreover, since the proposed method can support different NAFs, this brief improves the quantized model accuracy by selecting an appropriate NAF from Swish, GELU, Mish and PReLU. Compared to the baseline (parameters are FP32, NAF is ReLU), the accuracy of quantized ResNet-50 and MobileNet is increased by 2.65% and -0.33%.