4K detector, which is significantly less than the megapixel detectors found in affordable, consumer cameras. Unfortunately, feature size reductions, as predicted by Moore’s Law, will only result in minor improvements. For this reason, alternate technologies such as Resonant Tunneling Diodes and Neuron-Bipolar Junction Transistors are being explored. Also, CNN processor architecture is being re-evaluated. For example, Star-CNN processors, where one analog multiplier is time-shared between multiple processor units, have been proposed and are expected to result in processor unit reduction size of 80%. Digital CNN processors, FPGA Although not nearly as fast and energy efficient, digital CNN processors do not share the problems of process variation and feature size of their analog counterparts. This allows digital CNN processors to include nested processor units, non-linearities, etc. In addition, digital CNN are more flexible, cost less and are easier to integrate. The most common implementation of digital CNN processors uses an FPGA. Eutecus, founded in 2002 and operating in Berkeley, provides intellectual property that can be synthesized into an Altera FPGA. Their digital 320x280, FPGA-based CNN processors run at 30 frame/s and there are plans to make a fast digital ASIC. Eustecus is a strategic partner of AnaLogic computers, and their FPGA designs can be found in several of AnaLogic’s products. Eutecus is also developing software libraries to perform tasks including but not limited to video analytics for the video security market, feature classification, multi-target tracking, signal and image processing and flow processing. Many of these routines are derived using CNN-like processing. For those wanting to perform CNN simulations for prototyping, low-speed applications, or research, there are several options. First, there are precise CNN emulation software packages like SCNN 2000. If the speed is prohibitive, there are mathematical techniques, such as Jacobi’s Iterative Method or Forward-Backward Recursions that can be used to derive the steady state solution of a CNN processor. Lastly, digital CNN processors can be emulated on highly parallel, application-specific processors, such as graphics processors. Implementing neural networks using graphics processors is an area of further research. Holography, nanotechnology Researchers are also perusing alternate technologies for CNN processors. Although current CNN processors circumvent some of the problems associated with their digital counterparts, they do share some of the same long-term problems common to all semiconductor-based processors. These include, but are not limited to, speed, reliability, power-consumption, etc. AnaLogic Computers, is developing optical CNN processors, which combine optics, lasers, and biological