An Embedded Vision Services Framework for Heterogeneous Accelerators

Eduardo Gudis, Pullan Lu, David Berends, Kevin Kaighn, Gooitzen van der Wal, Gregory Buchanan, Sek Chai, Michael Piacentino; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 598-603

This paper describes an architecture framework using heterogeneous hardware accelerators for embedded vision applications. This approach leverages the recent singlechip heterogeneous FPGAs that combine powerful multicore processors with extensive programmable gate array fabric on the same die. We present a framework using an extensive library of pipelined real time vision hardware accelerators and a service-based software architecture. This field-proven system design approach provides embedded vision developers with a powerful software abstraction layer for rapidly and efficiently integrating any of hardware accelerators for applications such as image stabilization, moving target indication, contrast normalization enhancement, and others. The framework allows the service-based software to take advantage of the hardware acceleration blocks available and perform the remainder of the processing in software. As performance requirements increase, more hardware acceleration can be added to the FPGA fabric, thus offloading the main processor.