Voltage references represent important VLSI structures, having multiple appli- tions in analog and mixed-signal circuits: measurement equipment, voltage re- lators, temperature sensors, data acquisition systems, memories, or AD and DA converters. Operating as a subcircuit in a complex system, an important requi- ment for this class of circuits is represented by the possibility of implementation in the existing technology, using the available active and passive devices. The most important performances of a voltage reference circuit are represented by temperature behavior, power supply rejection ratio, transient response and, for the latest designs, by low-power low-voltage operation. Depending on the load - quirements, the output of the circuit can be regulated or unregulated. In order to reduce the sensitivity of the reference voltage with respect to the supply voltage variations, modi?ed cascode structures can be implemented, a trade-off between line regulation and low-voltage operation being necessary in this case. A large bandwidth of the voltage reference improves the transient behavior of the circuit, implying also a good noise rejection. Referringtothe possibilities ofimplementinga voltagereferencecircuit,two d- ferent approaches could be identi?ed: voltage-mode and current-mode topologies, being also possible to design a mixed-mode voltage reference.

th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.

This volume features the refereed proceedings of the 17th International Workshop on Power and Timing Modeling, Optimization and Simulation. Papers cover high level design, low power design techniques, low power analog circuits, statistical static timing analysis, power modeling and optimization, low power routing optimization, security and asynchronous design, low power applications, modeling and optimization, and more.