Event-based vision cameras emulate the functionality of mamalian retina and promise to be a low-latency, energy efficient sensory front-end for machine perception. Despite the large-scale effort to deploy these sensors in a variety of scenarios, a proportionally small amount of effort has been devoted to the design and analysis of embedded architectures that process address events adjacent to the sensor. In this paper, a neuromorphic signal processing pipeline is reported which sparsifies the event stream thereby reducing energy consumption, increasing the signal-to-noise ratio, and improving downstream algorithm performance. It is integrated within a system-on-chip platform that will allow for the prototyping of different standards compliant, hardware modules within a embedded processing framework. We report two such modules which provides adaptive throughput management, spatiotemporal filtering, and programmable feature extraction.