Computational Imaging in general has high compute requirements, this talk intends to provide a starting point to advance computational imaging towards real-time.  Today's image sensors, as most compute technologies advance at Moore's law pace in terms of pixel size, however, this drives the image processing requirements to advance at a quadratic rate, fast outpacing compute performance - this requires a paradigm shift in compute architectures to compensate and take advantage of image sensors advancements.  Specifically, the data bottleneck present in current architectures is examined as a limiting factor - this talk explores potential solutions that could bridge this increasing gap and could offer computational imaging a path to go online.