MT-9. Energy-efficient bio-inspired devices and architectures accelerate route to brain-like computing
Siegfried Karg (IBM Research, Zurich), Aida Todri-Sanial (LIRMM-CNRS, France), Bernabé Linares Barranco (IMSE-CNM, Sevilla, Spain), M.J. Avedillo (IMSE-CNM, Sevilla, Spain), T. Serrano-Gotarredona (IMSE-CNM, Sevilla, Spain)
Neuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems. By mimicking the human brain and nervous system, these computing architectures are excellent candidates for solving complex and large-scale associative learning problems. The mini-tutorial is based on the recently EU-funded NeurONN project. In NeurONN, we are developing an alternative neuromorphic computing paradigm based on energy-efficient devices where the information is encoded in the phase of coupled oscillating neurons or an oscillatory neural network (ONN). In this mini-tutorial, we aim to cover various aspects from devises, architecture design to algorithms to implement ONNs.
Siegfried Karg is Research Staff Member at IBM Research Zurich. He holds a Ph. D. degree in physics from Univ. Bayreuth (Germany). His current research fields are on 1D electronic properties of nanostructures and brain-inspired computing applications.
Aida Todri-Sanial is a Director of Research at CNRS, France. She holds a Ph. D. degree in Computer Engineering from Univ. of California Santa Barbara. Her current research fields are on 1D/2D nanomaterials for devices and circuits, and design of novel computing paradigms such as neuromorphic and quantum computing.
Bernabe Linares Barranco is a Full Professor at the Institute of Microelectronics of Seville. His research is on circuit design for telecommunication circuits, VLSI emulators of biological neurons, VLSI neural-based pattern recognition systems, hearing aids, precision circuit design for instrumentation equipment, bio-inspired VLSI vision processing systems, VLSI transistor mismatch parameters characterization, and memristors-based learning neuromorphic architectures.
Maria J. Avedillo is a Full Professor in the Department of Electronics and Electromagnetism of the University of Seville (US) and a member of the Instituto de Microelectrónica de Sevilla (IMSE), currently a joint centre of CSIC and US. Her research is focused on the study of VLSI integrated circuit design and test methodologies, the development of logic synthesis algorithms, the study of non-Boolean logic, both as regards the electrical realization of its components, as well as its use as a computational model in digital design and, more recently, in the design of circuits using emerging devices.