Our goal is to investigate the use of radically new implementation technology to enhance Intelligent Computing (IC) with new algorithms and new architectural and design techniques. We are focusing this work on recognition problems in computer vision. The algorithms we are studying have their origin in neuroscience, but they are significant abstractions of those algorithms, with the goal of retaining the essence of the computation while dropping many of the biological details. The 1st assumption is that these algorithms constitute a promising approach to achieving improved levels of IC. The 2nd assumption is that scaling to very large networks is a necessary requirement of intelligent computing, and the algorithms we are using do scale. The 3rd assumption is that CMOS will never give us the algorithm scaling we need at a reasonable cost/performance for scaled implementations of these algorithms. Thus we need to move to a far denser medium. Our 4th assumption then is that hybrid CMOS / nanogrids (CMOL)CMOL is the most promising implementation technology on the horizon. Consequently the goal of the research proposed here is to implement massively parallel, statistically based algorithms in CMOL, which is our solution to the general problem. Our approach is to take a real application with a number of different algorithmic stages and study the mapping of that stage to CMOL. The design spectrum for each stage will be based on a concept we call virtualization. The performance/price of a particular implementation is determined by the degree of virtualization, which in turn is determined by the algorithm and its dynamic behavior.
