Standard CNNs Research Articles

Pyramidal cells: a novel class of adaptive coupling cells and their applications for cellular neural networks

A significant increase in the information processing abilities of CNN's demands powerful information processing at the cell level. In this paper, the defining formula, the main properties, and several applications of a novel coupling cell are presented. Since it is able to implement any Boolean function, its functionality expands on those of digital RAMs by adding new capabilities such as learning and interpolation. While it is able to embed all previously accumulated knowledge regarding useful binary information processing tasks performed by standard CNNs, the pyramidal universal cell provides a broader context for defining other useful processing tasks, including extended gray scale or color image processing as well. Examples of applications in image processing are provided in this paper. Implementation issues are also considered. Assuming some compromise between area and speed, a VLSI implementation of CNNs based on pyramidal cells offers a speedup of up to one million times when compared to corresponding software implementations.

A programmable VLSI architecture based on multilayer CNN paradigms for real‐time visual processing

A new digital VLSI architecture has been presented for the implementation of discrete-time multilayer CNNs. At functional level, the architecture is organized as 12 layers of 64 × 64 cells which interact as specified by a set of 3D generalized templates. At structural level the application of cloning templates occurs in a set of processing units programmed by instruction masks, generated on the basis of the algorithm to be emulated. It is demonstrated that this architecture is applicable to multilayer algorithms for visual processing and also to standard CNNs, including those that use sequences of templates or that work in parallel. Simulations evidence the high efficiency of this implementation.

A programmable VLSI architecture based on multilayer CNN paradigms for real‐time visual processing

A new digital VLSI architecture has been presented for the implementation of discrete-time multilayer CNNs. At functional level, the architecture is organized as 12 layers of 64 × 64 cells which interact as specified by a set of 3D generalized templates. At structural level the application of cloning templates occurs in a set of processing units programmed by instruction masks, generated on the basis of the algorithm to be emulated. It is demonstrated that this architecture is applicable to multilayer algorithms for visual processing and also to standard CNNs, including those that use sequences of templates or that work in parallel. Simulations evidence the high efficiency of this implementation.