Hardware implementation of KLMS algorithm using FPGA

Fast and accurate machine learning algorithms are needed in many physical applications. However, the learning efficiency is badly subjected to the intensive computation. Knowing that hardware implementation could speed up computation effectively, we use a FPGA hardware platform to implement an on-line kernel learning algorithm, namely the kernel least mean square (KLMS) which adopts the simple survival kernel as the Mercer kernel. By using an on-line quantization method and pipeline technology, the requirement of hardware resources and computation burden can be reduced significantly and the data processing speed can be accelerated apparently without losing accuracy. Finally, a 128-way parallel FPGA platform which works at 200MHz is implemented. It could achieve an average speedup of 6553 versus Matlab running on a 3GHz Intel(R) Core(TM) i5-2320 CPU.