Seminar: From myriads of neurons and fractions of seconds: accuracy and efficiency in neuroscientific benchmarks

by Dr Johanna Senk (University of Sussex, Brighton, UK and Juelich Research Centre, Juelich, Germany)

Thursday 24 Oct 2024, 14:30 → 15:30 Europe/Rome

Sala Consiliare (Dipartimento di Fisica)

Performance benchmarks already drive AI research. Computational neuroscientists investigate brain-like computing by constructing and simulating network models that integrate insights about the structure and function of the brain. The local cortical microcircuit of Potjans & Diesmann (2014) is considered a universal building block of the brain and its characteristics play an essential role in the development of simulation technology for neuroscientific research. On the example of this model, different simulators (using CPUs, GPUs, FPGA and dedicated neuromorphic hardware) have been compared in terms of accuracy, time-to-solution, and energy-to-solution, leading to significant performance gains for the community in just a few years.

This seminar will discuss the benchmarking endeavors around the microcircuit model and explore future directions, for example, simulating even more complex neuroscientific models while further improving performance and energy efficiency of simulation technologies.

 

Short Bio: Dr. Johanna Senk is a Lecturer in Computer Science at the School of Engineering & Informatics, University of Sussex, UK. She is also the leader of the team “Future Simulation Architectures” at the Institute for Advanced Simulation (IAS-6), Juelich Research Centre, Germany. Dr. Senk studied Physics with a focus on Solid-State Physics and Computational Physics at RWTH Aachen University, Germany, and the University of Trieste, Italy. She received her Dr. rer. nat. degree from the faculty of Mathematics, Computer Science and Natural Sciences of RWTH Aachen University. She performed the research for her PhD and Postdoc in the interdisciplinary field of Computational Neuroscience at Juelich Research Centre. Her research is concerned with uncovering principles of neural network connectivity, including spatial organization and structural plasticity, and novel simulation technology.