CNN Universal Machine Research Articles

CNN universal chips crank up the computing power

New cellular neural network chips, with stored-program capability and analog-and-logic architecture, are poised to challenge all-digital processing. In this article, we highlight the key ideas leading to the CNN Universal Machine, using simple circuit interpretations. We also illustrate the system, software, and application aspects.

Application of a cellular neural network to facial expression animation and high-level image processing

The cellular neural network (CNN) is used to animate facial expressions of a human being. First, the change in facial expressions is regarded as a smooth 2D transformation which is restricted by a bending energy function and displacements of some key-points. Second, the parameters of the CNN are determined by comparing the bending energy function with the energy function of the CNN. Finally, the CNN is used to realize the transformation by minimizing its energy function. Also, the CNN is used to model some visual illusions which are frequently used in psychological tests. First, the retinal induction field is modelled by using a template. The comparison of this CNN model with the real physiological measurements is presented. Then, based on this template, the CNN universal machine is used to model four types of visual illusions: subjective contour illusion (Kanizsa illusion), size illusion (Mueller-Lyer illusion, Ponzo illusion), direction and location illusion (angular illusion of location, Poggendorff illusion) and contrast illusion (Herring illusion). Computer simulations are provided for animating facial expressions and modelling visual illusions.

Application of a cellular neural network to facial expression animation and high‐level image processing

The cellular neural network (CNN) is used to animate facial expressions of a human being. First, the change in facial expressions is regarded as a smooth 2D transformation which is restricted by a bending energy function and displacements of some key-points. Second, the parameters of the CNN are determined by comparing the bending energy function with the energy function of the CNN. Finally, the CNN is used to realize the transformation by minimizing its energy function. Also, the CNN is used to model some visual illusions which are frequently used in psychological tests. First, the retinal induction field is modelled by using a template. The comparison of this CNN model with the real physiological measurements is presented. Then, based on this template, the CNN universal machine is used to model four types of visual illusions: subjective contour illusion (Kanizsa illusion), size illusion (Mueller-Lyer illusion, Ponzo illusion), direction and location illusion (angular illusion of location, Poggendorff illusion) and contrast illusion (Herring illusion). Computer simulations are provided for animating facial expressions and modelling visual illusions.

The CNN Universal Machine is as universal as a Turing Machine

It is shown that the simplest integrated circuit implementations of the CNN Universal Machine can play the "game of life", and are therefore equivalent to Turing Machines. In addition, a constructive proof is given for the direct implementation of general first-order cellular automata on such machines.

A CNN UNIVERSAL CHIP IN CMOS TECHNOLOGY

This paper describes the design of a programmable cellular neural network (CNN) chip with added functionalities similar to those of the CNN universal machine. The prototype contains 1024 cells and has been designed in a 1.0 μm, n-well CMOS technology. Careful selection of the topology and design parameters has resulted in a cell density of 31 cells mm -2 and around 7-8 bits accuracy in the weight values. Adaptive techniques have been employed to ensure accurate external control and system robustness against process parameter variations.

A CNN UNIVERSAL CHIP IN CMOS TECHNOLOGY

This paper describes the design of a programmable cellular neural network (CNN) chip with added functionalities similar to those of the CNN universal machine. The prototype contains 1024 cells and has been designed in a 1·0 μm, n-well CMOS technology. Careful selection of the topology and design parameters has resulted in a cell density of 31 cells mm−2 and around 7–8 bits accuracy in the weight values. Adaptive techniques have been employed to ensure accurate external control and system robustness against process parameter variations.

The analogic cellular neural network as a bionic eye

AbstractThe conception of the CNN universal machine has led quite naturally to the invention of the analogic CNN bionic eye (henceforth referred to simply as the bionic eye). the basic idea is to combine the elementary functions, the building blocks, of the retina and other 2 1/2 D sensory organs, algorithmically, in a stored programme of a CNN universal machine, through the use of artificial analogic programmes. the term bionic is defined in a rigorous way: it is a nonlinear, dynamic, spatiotemporal biological model implemented in a stored programme electronic (optoelectronic) device; this device is in our case the analogic CNN universal machine (or chip).The aim of this paper is to report on this new invention, particularly to electronic and computer engineers, in a tutorial way.We begin by summarizing (1) the biological aspects of the range of retinal function (the retinal universe), (2) the CNN paradigm and the CNN universal machine architecture and (3) the general principles of retinal modelling in CNN. Next we describe new CNN circuit and template design innovations that can be used to implement physiological functions in the retina and other sensory organs using the CNN universal machine. Finally we show how to combine given retinal functional elements implemented in the CNN universal machine with analogic algorithms to form the bionic retina. the resulting system can be used not only for simulating biological retinal function but also for generating functions that go far beyond biological capabilities. Several bionic retina functions, different topographic modalities and analogic CNN algorithms can then be combined to form the analogic CNN bionic eye. the qualitative aspects of the models, especially the range of dynamics and accuracy considerations in VLSI optoelectronic implementations, are outlined. Finally, application areas of the bionic eye and possibilities of constructing innovative devices based on this invention (such as the bionic eyeglass or the visual mouse) are described.

Analogic CNN algorithms for some image compression and restoration tasks

This paper presents analogic cellular neural network universal machine algorithms for some important image processing tasks. Templates are given for skeletonizing black-and-white and grey-scale images. A method is presented enhancing the quality of copying machines by removing scratches from the copied images. Finally, a similar method is used to enlarge images. All templates use only a nearest neighborhood, therefore being well suited for VLSI implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The CNN universal machine: an analogic array computer

A new invention, the cellular neural network (CNN) universal machine and supercomputer, is presented. This is the first algorithmically programmable analog array computer having real-time and supercomputer power on a single chip. The CNN universal machine is described, emphasizing its programmability as well as global and distributed analog memory and logic, high throughput via electromagnetic waves, and complex cells that may be used also for simulating a broad class of PDEs. Its implementation, the CNN universal chip, is also described, along with its use of a multichip supercomputer. Other types of hardware implementations are also briefly discussed. Details of the algorithmic aspects of the new type of analogic (analog logic) algorithms, as well as the analogic software (language, compiler, machine code, etc.) are explained, along with a brief description of the available CNN workstation for implementing and simulating these new concepts. A broad range of applications is reviewed, including neuromorphic computing, programmable physics, programmable chemistry, and programmable bionics.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>