DNN-based Cancer Recurrence Predictor using FPGA.

This paper proposes a hardware-friendly cancer recurrence prediction Deep Neural Network (DNN) model. For hardware implementation, we used 16-bit integer quantization of the original DNN model, and the parameters were reduced by about 37.41%. The proposed hardware accelerator architecture is implemented on the Xilinx Kintex UltraScale+ FPGA.