Hall 3, 19/09/2019 (15:00-15:30)
The potential of neuromorphic computing has been only partially explored so far. Compared to biological neural networks, current neuron/dendrite models are simple, the networks small and learning models appear to be rather basic. The challenge is to exploit a wider range of biological principles from the hardware level up and from the cognitive level down, by developing the related algorithms and programming framework, in order to create neuromorphic technologies that can outperform current systems in terms of size, scalability, connectivity, power consumption, ease of training, flexibility, reliability or any other relevant metrics.
The scope is to target new computational substrates and engines, based on new materials and engineering principles for efficient and low-power neuromorphic computing; together with new theories, architectures and algorithms for neuromorphic computation, learning and adaptation/plasticity for and in such new neuromorphic hardware.
The call is designed to contribute to:
• Bring neuromorphic engineering at the level where it can be benchmarked in terms of performance, power consumption, size, latency or other relevant metric
• Pave the way to market take-up of neuromorphic computing in a range of existing and new application areas
• Stimulate the emergence of a European innovation ecosystem around neuromorphic engineering, well beyond the world of research alone.
Track: FET, Innovation & EOSC
2 Presentations: Submit your own
- Math Framework for Bio-Inspired Signal Processing: Quasi-Isometry as Key Concept
- End-to-End Deep Learning Towards Neuromorphic Computing
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