See the detailed program here.
Neuromorphic computing is an interdisciplinary approach to building physical computation structures that are brain-inspired. This approach can enable the deployment of AI and traditional algorithms, by dramatically reducing task-computation time and system energy consumption. While modern AI implements Neural Network abstractions in software, Neuromorphic hardware mimics Biological Neural Networks. Furthermore, Neuromorphic computing is much more than simply being an efficient hardware implementation of modern AI; it is defined by three ideas used separately or in combination: (i) the use of distributed, dynamic and efficient Spiking Neural Networks (SNNs), or Compute-in-Memory networks, (ii) the use of advanced materials and devices in addition to those based on Silicon, and (iii) application to real-time processing tasks, such as video/audio signal processing, motion planning, or data streaming.
This technology is used both for lowering the energy consumed for running heavy algorithms on computers, and also for higher-throughput interfacing of computers with the physical world. Development of neuromorphic architectures, integrated circuits and systems is an imminent research field for lowering energy requirements of the heavy computations needed in AI such as Deep learning algorithms, promising at least a ten thousand-fold drop in energy usage, and leading to real-time AI capacity in power-constrained and edge applications
Invited key-note speakers
Alexander Stasik, SINTEF, Norway - Reservoir computing: Possibilities and challenges
This talks give an introduction into the field of reservoir computing, starting from the fundamental concepts, covering some platforms and implementations of reservoirs and discussing some recent developments.
Federico Corradi, TU Eindhoven- Neuromorphic Systems for Embedded Applications
In this talk, I will discuss the principles underpinning spiking neural networks, focusing on state-of-the-art training algorithms and hardware implementations that emphasize event-driven computation and sensory processing.
Alessandro Pierro, the Intel Neuromorphic Computing Lab - Accelerating Recurrent Networks with Neuromorphic Hardware-Algorithm Co-Design
This talk will cover the motivation and principles behind modern neuromorphic architectures, focusing on the Intel Loihi 2 research chip as a representative example.
Contributed talks session
Attendees may also present short-talks (20 minutes), on any topic in the broad field of Neuromorphic computing. You may choose to talk about your specific technical project on Neuromorphic hardware and algorithms, or you may choose to speculate on business/social aspects of the expected useful applications, or on technical challenges to be overcome etc.
If you want to present, then please submit a title and an abstrct (of no more 250 words) under the registration link.
Deadline: 16:00 (CET) 5th of October,