in different environments and still function if some of the processing units are disabled. Communication systems The variety of dynamical behavior seen in CNN processors make them intriguing for communication systems. Chaotic communications using CNN processors is being researched due to their potential low power consumption, robustness and spread spectrum features. The premise behind chaotic communication is to use a chaotic signal for the carrier wave and to use chaotic phase synchronization to reconstruct the original message. CNN processors can be used on both the transmitter and receiver end to encode and decode a given message. They can also be used for data encryption and decryption, source authentication through watermarking, detecting of complex patterns in spectrogram images (sound processing), and transient spectral signals detection. CNN processors are neuromorphic processors, meaning that they emulate certain aspects of biological neural networks. The original CNN processors were based on mammalian retinas, which consist of a layer of photodetectors connected to several layers of locally coupled neurons. This makes CNN processors part of an interdisciplinary research area whose goal is to design systems that leverage knowledge and ideas from neuroscience and contribute back via real-world validation of theories. CNN processors have implemented a real-time system that replicates mammalian retinas, validating that the original CNN architecture chosen modeled the correct aspects of the biological neural networks used to perform the task in mammalian life. However, CNN processors are not limited to verifying biological neural networks associated with vision processing; they have been used to simulate dynamic activity seen in mammalian neural networks found in the olfactory bulb and locust antennal lobe, responsible for pre-processing sensory information to detect differences in repeating patterns. CNN processors are being used to understand systems that can be modeled using simple, coupled units, such as living cells, biological networks, physiological systems, and ecosystems. The CNN architecture captures some of the dynamics often seen in nature and is simple enough to analyze and conduct experiments. They are also being used for stochastic simulation techniques, which allow scientists to explore spin problems, population dynamics, lattice-based gas models, percolation, and other phenomena. Other simulation applications include heat transfer, mechanical vibrating systems, protein production, Josephson Junction problems, seismic wave propagation, and geothermal structures. Instances of 3d CNN have been used to prove certain emergent phenomena in complex systems, establishing a link between art, dynamical systems and VLSI technology. CNN processors have been used to