Reconfigurable In-Sensor Architectures for High Speed and Low Power In-situ Image Analysis

Principal Investigator: Christophe Bobda

Sponsor: NSF

Start Date: June 14, 2019

End Date: July 31, 2021

Amount: $289,815

Abstract

Cameras are pervasively used for surveillance and monitoring applications and can capture a substantial amount of image data. The processing of this data, however, is either performed a posteriori or at powerful backend servers. While a posteriori and non-real-time video analysis may be sufficient for certain groups of applications, it does not suffice for applications such as autonomous navigation in complex environments, or hyper spectral image analysis using cameras on drones, that require near real-time video and image analysis, sometimes under SWAP (Size Weight and Power) constraints. The goal of this research is the design of a highly parallel, hierarchical, reconfigurable and vertically-integrated 3D sensing-computing architecture (XPU), along with high-level synthesis methods for real-time, low-power video analysis.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1946088