This work presents a processing-in-sensor platform leveraging magnetic devices as a flexible and efficient solution for real-time and smart image processing in AI devices. The main idea is to combine the typical sensing mechanism with an intrinsic coarse-grained convolution operation at the edge to remarkably reduce the power consumption of data conversion and transmission to an off-chip processor imposed by the first layer of deep neural networks. Our initial results demonstrate acceptable accuracy on MNIST and SVHN image data-sets, while the proposed platform substantially reduces data conversion and transmission energy compared with a baseline sensor-CPU platform.