Moderator: Stefano COZZINI (Area Science Park) Synopsis Advanced computing and high-performance computers (HPC) have been used to model, simulate and analyse scientific problems in different fields, from astrophysics to climate science, since the dawn of the modern computing era. In the last half century, the models studied by HPC systems have increased exponentially both in scale and resolution. Next-generation computers are expected to operate at the exascale (10^18 flops per second), thanks to new types of accelerators, such as the latest generation of GPUs installed on exascale supercomputers (NVIDIA Grace Hopper). The downside of GPU computing is power consumption. Data computation and data transfer are responsible for a large part of this consumption, and the rapid development of Machine Learning and AI neural network models is adding even more demand. Since we have reached the end of Moore’s law, the need for a shift in perspective on HPC technologies has become necessary. Big data analysis and exascale computing require a data-centric computing approach. This new paradigm is characterised by high-speed memory transfer, and a hardware system revolution, based on new technologies (e.g. Neuromorphic chips or Quantum Accelerators) and new infrastructures better suited for dynamic workflows (e.g. cloud). The performance of the computing system, in particular the energy efficiency, sets the fundamental limit of AI/ML capability. This panel session will investigate the capabilities of new technologies (e.g. Neuromorphic computing, quantum) and new HPC infrastructures (e.g. cloud), highlighting the pros and cons of each.