Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
The paper presents an influence of leakage effect observed in capacitive analog memories on learning process in hardware implemented Kohonen neural networks with MOS transistors used as switches connected with information holding capacitors. The learning results, i.e. variations (adaptations) of weight values, strongly depend on the transistor leakage currents. This is a cause for some quantization error associated with the weight adaptations during the network training. The unwanted leakage influence can be minimized in several ways discussed in this paper. As expected, the observed leakage influence on the memory storage time rises with an increase of temperature. This has been verified by means of computer simulations (Matlab, HSpice) as well as measurements of a prototyped Kohonen network chip (0.18 mu m CMOS process).