frame-grabber. This allowed CNN processing to be easily included in a desktop computer. In 2006, AnaLogic Computers developed their Bi-I Ultra High Speed Smart Camera product line, which includes the ACE 4K processor in their high-end models. In 2006, Roska et al. published a paper on designing a Bionic Eyeglass for AnaLogic. The Bionic Eyeglass is a dual-camera, wearable platform, based on the Bi-I Ultra High Speed Smart Camera, designed to provide assistance to blind people. Some of its functions include route number recognition and color processing. Analog CNN processors Some researchers developed their own custom analog CNN processors. For example: A research team from University degli Studi di Catania made one in order to generate gaits for a hexapod robot. Chung-Yu Wu and Chiu-Hung Cheng from National Chiao Tung University designed a RM-CNN processor to learn more about pattern learning and recognition. Researchers from the National Lien-Ho Institute of Technology (W. Yen, R. Chen and J. Lai) developed a Min-Max CNN (MMCNN) processor to learn more about CNN dynamics. Despite their speed and low power consumption, there are some significant drawbacks to analog CNN processors. First, analog CNN processors can potentially create erroneous results due to environment and process variation. In most applications, these errors are not noticeable, but there are situations where minor deviations can result in catastrophic system failures. For example, in chaotic communication, process variation will change the trajectory of a given system in phase space, resulting in a loss of synchronicity/stability. Due to the severity of the problem, there is considerable research being performed to ameliorate the problem. Some researchers are optimizing templates to accommodate greater variation. Other researchers are improving the semiconductor process to more closely match theoretical CNN performance. Other researchers are investigating different, potentially more robust CNN architectures. Lastly, researchers are developing methods to tune templates to target a specific chip and operating conditions. In other words, the templates are being optimized to match the information processing platform. Not only does process variation limit what can be done with current analog CNN processors, it is also a barrier for creating more complex processing units. Unless this process variation is resolved, ideas such as nested processing units, non-linear inputs, etc. cannot be implemented in a real-time analog CNN processor. Also, the semiconductor "real estate" for processing units limits the size of CNN processors. Currently the largest AnaVision CNN-based vision processor consists of a