Scalable Pitch-Constrained Neural Processing Unit for 3D Integration with Event-Based Imagers

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 energy-efficiency 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.