3-D IC design architecture for image understanding

Image understanding technology has broad application. Especially on Today´s LCD display panel and LED lighting device production, it can be applied to test and evaluate the quality of the display panel and lighting products. For the industrial applications such as bin picking, assembly and inspection, image understanding technology can be used to support those applications at real-time rates. Another area of applicability is that of automatic interpretation of aerial or satellite imagery for use in GPS or computer aided cartography. IC design for machine vision and image understanding is one of the most computationally intensive domains of artificial intelligence research. It requires that an interpretation of the changing scene be updated with every new video frame, once every thirtieth of a second. With three quarters of a million color-intensity data values which comprise the picture elements or pixels of the image, performing a single operation on each of these pixels requires an execution rate of about twenty-three million instructions per second for just to keep up with the input. Of course far more than one operation per pixel is necessary. The image understanding is not limited to the data processing in the visible spectrum; it should have more applicable to computer aided systems for image signal processing such as avionics flight control, synthetic array radar and sonar systems etc... The bandwidth requirement of these systems is even higher. In order to achieve these enormous bandwidths a 3-D IC parallel processing design architecture and its algorithm will be discussed. With the advancement of the high density packaging and the level of 3D IC integration technologies available today, it maybe the best approach to achieve these bandwidths by implementing the SIMD (Single Instruction Multiple Data) and the BSWP (Bit Serial Word Parallel) processing technologies into the 3D IC circuitry. The SIMD and the BSWP will be discussed and analyzed as approaches t- achieving these enormous bandwidths requirement for machine vision and image understanding.