Event-based imagers are bio-inspired sensors presenting intrinsic High Dynamic Range and High Acquisition Speed properties. However, noisy pixels and asynchronous readout result in poor energyefficiency and excessively large output data rates. In this work, we use Convolutional Spiking Neural Network filters to compensate these drawbacks and reduce output bandwidth by 10x. We designed a neuromorphic core as a distributable block that benefits from 3D integration technology with direct and parallel access to 32x32 pixels, enabling reduced frequency operation. Post-layout simulations depict a peak energy efficiency with 2.83pJ per Synaptic Operation (equivalent to 0.093fJ/event/pix) at the nominal literature input event rate.