Artificial neural network implementation in FPGA: A case study
Shuai Li, Ken Choi, Yun-Sik Lee · 2016
Artificial Neural Network (ANN) is very powerful to deal with signal processing, computer vision and many other recognition problems. In this work, we implement basic ANN in FPGA. Compared with software, the FPGA implementation can utilize parallelism to speedup processing time. Additionally, hardware implementation can save more power compared with CPU/GPU. Our ANN in FPGA has a high learning ability, for logical XOR problem, which reduced the error rate from 10-2to 10-4.