and holographic memories. What initially was technology exploration resulted in a 500x500 CNN processor able to perform 300 giga-operations per second. Another promising technology for CNN processors is nanotechnology. One nanotechnology concept being investigated is using single electron tunneling junctions, which can be made into single-electron or high-current transistors, to create McCulloch-Pitts CNN processing units. In summary, CNN processors have been implemented and provide value to their users. They have been able to effectively leverage the advantages and address some of the disadvantages associated with their underling technology, i.e. semiconductors. Researchers are also transitioning CNN processors into emerging technologies. Therefore, if the CNN architecture is suited for a specific information processing system, there are processors available for purchase (as there will be for the foreseeable future). Applications CNN researchers have diverse interests, ranging from physical, engineering, theoretical, mathematical, computational, and philosophical applications. Image processing CNN processors were designed to perform image processing; specifically, real-time ultra-high frame-rate (>10,000 frame/s) processing for applications like particle detection in jet engine fluids and spark-plug detection. Currently, CNN processors can achieve up to 50,000 frames per second, and for certain applications such as missile tracking, flash detection, and spark-plug diagnostics these microprocessors have outperformed a conventional supercomputer. CNN processors lend themselves to local, low-level, processor intensive operations and have been used in feature extraction, level and gain adjustments, color constancy detection, contrast enhancement, deconvolution, image compression, motion estimation, image encoding, image decoding, image segmentation, orientation preference maps, pattern learning/recognition, multi-target tracking, image stabilization, resolution enhancement, image deformations and mapping, image inpainting, optical flow, contouring, moving object detection, axis of symmetry detection, and image fusion. Due to their processing capabilities and flexibility, CNN processors have been used and prototyped for novel field applications such as flame analysis for monitoring combustion at a waste incinerator, mine-detection using infrared imagery, calorimeter cluster peak for high energy physics, anomaly detection in potential field maps for geophysics, laser dot detection, metal inspection for detecting manufacturing defects, and seismic horizon picking. They have also been used to perform biometric functions such as fingerprint recognition, vein feature extraction, face tracking, and generating visual stimuli via emergent patterns to gauge perceptual resonances. Biology and medicine CNN processors have been used for medical and biological research in performing automated nucleated cell counting for detecting hyperplasia, segment images into anatomically and pathologically meaningful regions, measure and quantify cardiac function, measure the timing of neurons, and