FAMOUS2

This project is the port to hardware of a proof of concept realized in simulation, in which IBM Spiking Neural Units (SNUs) and neuromorphic vision were exploited in an asset monitoring use case. An existing vision algorithm for object identification was transferred in the neuromorphic paradigm, integrating optical flow computed with SNUs. This method will be implemented in hardware and optimized to highlight neuromorphic hardware’ s benefits: great sparsity, small latency and low power consumption. With those properties, this approach offers an alternative to existing object identification methods, such as WLAN or Bluetooth.

 The simulated use case is assets monitoring with a drone equipped with a neuromorphic vision sensor. Light beacons emitting a visual pattern are attached to assets, such as workers or packages, and are blinking with a pattern, like Morse code, to transmit useful information. The aim of this project is to create a similar use case in hardware and use it as a demonstrator showcased in the fortiss labs, with an embedded event camera on a drone.