Summary
Event-based imagers are an emerging class of sensors with significant demonstrated advantages relative to traditional cameras. Because they operate asynchronously and only transmit data from pixels that have changed, they have been shown to produce over 100x less data in sparse scenes relative to traditional focal plane arrays (FPAs). This leads directly to 100x lower latency at 100x lower power.
Despite their inherent advantages, existing event-based cameras are not currently compatible with Department of Defense (DoD) applications as DoD scenarios are highly cluttered and dynamic.
The Fast Event-based Neuromorphic Camera and Electronics (FENCE) program seeks to develop an integrated event-based infrared (IR) FPA with embedded processing to overcome the challenges faced by previous event-based cameras.
The FENCE FPA could solve the problems of timing accuracy and data sparsity to produce an infrared event-based neuromorphic imager consistent with military requirements. The FENCE FPA will consist of a low latency, high precision event-based readout matched to low power embedded neuromorphic processing capable of executing algorithms that use combined spatial and temporal (spatio-temporal) information, to produce a new intelligent sensor for tactical DoD applications.
The sole technical area is focused on the development of an asynchronous read-out integrated circuit (ROIC) capable of very low latency and power operation and a low power processing layer that integrates with the ROIC to identify salient spatio-temporal signals. The goal is to integrate the ROIC and the processing layer together to demonstrate a FENCE sensor operating at low power (< 1.5 W).