Emulation of Bursting Neurons in Neuromorphic Hardware based on Phase-Change Materials

Master's Thesis from the year 2012 in the subject Physics - Other, grade: 1,7, RWTH Aachen University (1. Physikalisches Institut (IA)), language: English, abstract: In the history of computing hardware,Moore's law, named after Intel co-founder Gordon E. Moore, describes a long-termtrend, whereby the number of transistors that can be placedinexpensively on an integrated circuit doubles approximately every two years. Becausethe number of transistors is crucial for computing performance, significant performancegains could be achieved simply through complementary metal-oxide-semiconductor (CMOS)transistor downscaling. AlthoughMoore s law, which was mentioned for the first time in 1965,turned out to persist for almost five decades, the nano era poses significant problems to theconcept of downscaling. Upon approaching the size of atoms, quantum effects, such asquantum tunneling, pose fundamental barriers to the trend. Furthermore, the conventionalcomputing paradigm based on the Von-Neumann architecture and binary logic becomesincreasingly inefficient considering the growing complexity of todays computational tasks. Hence, new computational paradigms and alternative information processing architecturesmust be explored to extend the capabilities of future information technology beyond digitallogic. A fantastic example for such an alternative information processing architecture is thehuman brain. The brain provides superior computational features such as ultrahigh densityof processing units, low energy consumption per computational event, ultrahigh parallelismin computational execution, extremely flexible plasticity of connections between processingunits and fault-tolerant computing provided by a huge number of computational entities. Compared to today s programmable computers, biological systems are six to nine orders ofmagnitude more efficient in complex environments. For instance: simulating five secondsof brain activity takes IBM s state-of-the-art supercomputer Blue Gene a hundred times aslong, i.e. 500 s, during which it consumes 1.4MWof power, whereas the power dissipation inthe human central nervous system is of the order of 10W. Thus, it is not only extremelyinteresting but in terms of computational progress also highly desirable to understand how information is processed in the human brain. The conceptual idea developed within theframework of this thesis tries to contribute to this intention. [...]