Lossless cellular neural networks

Since their introduction, Cellular Neural Networks [4] have turned out to be useful architectures for the solution of many problems, e.g. in image processing or in the simulation of partial differential equations. Therefore, there have been several attempts to introduce cell circuits suitable for large-scale integration [3]. Up to now, all of these cells need energy and therefore power supply. Just recently attempts have been made to build up circuitry being able to work without an external energy supply by using the energy stored in the initial state [1]. This principle can provide two major advantages. First, since no or at least not much energy is dissipated during computation, the circuit does not produce much heat. Therefore, there are no 'hot spots' in integrated circuits, which limit integration density and operation speed. Furthermore, since there is no need for a power supply, the absence of voltage supply lines supports a high integration density. In this work an architecture for the realisation of a lossless CNN is proposed. Further on, since standard learning algorithms turn out to fail for lossless systems, a way to amend these is introduced.