Learning on chip (LOC) is a challenging problem, which allows an embedded system to learn a model and use it to process and classify unknown data, adapting to new obser- vations or classes. Incremental learning of chip (ILOC) is more challenging. ILOC needs intensive computational power to train the model and adapt it when new data are observed, leading to a very difficult hardware implementation. We adress this issue by introducing a method based on the combination of a pre-trained Convolutional Neural Network (CNN) and majority vote, using Product Quantizing (PQ) as a bridge between them. We detail a hardware implementation of the proposed method validated on an FPGA target, with substantial processing acceleration with few hardware resources.