We present a striping noise compensation architecture for hyperspectral push-broom cameras, implemented on a Field-Programmable Gate Array (FPGA). The circuit is fast, compact, low power, and is capable of eliminating the striping noise in-line during the image acquisition process. The architecture implements a multi dimensional neural network (MDNN) algorithm for striping noise compensation previously reported by our group. The algorithm relies on the assumption that the amount of light impinging at the neighboring photo-detectors is approximately the same in the spatial and spectral dimensions. Under this assumption, two striping noise parameters are estimated using spatial and spectral information from the raw data. We implemented the circuit on a Xilinx ZYNQ XC7Z2010 FPGA and tested it with images obtained from a NIR N17E push-broom camera, with a frame rate of 25fps and a band-pixel rate of 1.888 MHz. The setup consists of a loop of 320 samples of 320 spatial lines and 236 spectral bands between 900 and 1700 nanometers, in laboratory condition, captured with a rigid push-broom controller. The noise compensation core can run at more than 100 MHZ and consumes less than 30mW of dynamic power, using less than 10% of the logic resources available on the chip. It also uses one of two ARM processors available on the FPGA for data acquisition and communication purposes.