4th Panhellenic Conference on Electronics and Telecommunications
Xanthi, 17-18 November 2017
Assistant Professor in the Faculty of Electrical and Computer Engineering
Technische Universität Dresden
received a Ph.D. Degree in Electronic Engineering from University College Dublin in 2006.
From 2006 to 2009 he worked as RFIC analog engineer at CSR Sweden AB. From 2009 to 2012 he was Research Assistant in the Department of Electronics and
Telecommunications at Politecnico di Torino. Since 2012 he is Assistant Professor in the Faculty of Electrical and Computer Engineering, Technische Universität Dresden.
His research interests lie in the area of nonlinear circuits and systems, networks of oscillators, Cellular Nonlinear Networks and memristors. Dr. Ascoli was honored
with the IJCTA 2007 Best Paper Award.
In April 2017 he was conferred the habilitation title as Associate Professor in Electrical Circuit Theory from the Italian Ministry of Education. Since 2014 he
is Management Committee Substitute for Germany in the COST Action IC1401 MemoCIS "Memristors - Devices, Models, Circuits, Systems, and Applications". He has
been Program Chair and Special Session Chair for the 15th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) in 2016.
Mem-adaptive computing by memristor circuits
The future electronics will strongly rely on the use of neuromorphic circuits to compute data in a
time- and energy-efficient fashion similarly as biological systems. Thereby, in order to overcome
the limits of conventional von Neumann architectures, several recent investigations on novel
signal processing paradigms adopt memristor neuron or synapse models, given their unique
feature to combine memory and data computing capabilities in the same physical nano-scale
volume. One interesting area where the use of memristors may find suitable application is in
bio-inspired artificial neural networks known as Cellular Neural Networks (CNN). These are
universal high-speed computing systems inherently based on the principle of “distributed
computing with memory” and endowed with stored programmability on board. The dynamic
behavior of these networks, first introduced by L.O. Chua in 1988, is exploited to accomplish
multi-dimensional signal processing tasks (e.g. to solve pattern recognition/classification
problems) or to model complex dynamics in biological systems (e.g. to capture pattern formation
and wave propagation in locally-active reaction-diffusion systems). The principle of “combined
sensing and computing” has been recently proposed also to control the motion of a limb of
the humanoid robot Myon more quickly and under a lower energy expenditure than it is
achieved under the state-of-the-art paradigm, without compromising on the adaptability to
changes in the nominal operating conditions.