Hardware implementation of a PCA learning network by an asynchronous PDM digital circuit

We have fabricated a PCA (Principal Component Analysis) learning network in a FPGA (Field Programmable Gate Array) by using an asynchronous PDM (Pulse Density Modulation) digital circuit. The generalized Hebbian algorithm is expressed in a set of ordinary differential equations and the circuits solve them in a fully parallel and continuous manner. The performance of the circuits was tested by a network with two-microphone inputs and two-speaker outputs. By moving a sound source right and left in front of the microphones, the first principal weight vector could continuously track the sound direction in real time.