Brain inspired hardware architectures - Can they be used for particle physics ?

About

After their inception in the 1940s and several decades of moderate success, artificial neural networks have recently demonstrated impressive achievements in analysing big data volumes. Wide and deep network architectures can now be trained using high performance computing systems, graphics card clusters in particular.

Despite their successes these state-of-the-art approaches suffer from very long training times and huge energy consumption, in particular during the training phase. The biological brain can perform similar and superior classification tasks in the space and time domains, but at the same time exhibits very low power consumption, rapid unsupervised learning capabilities and fault tolerance.

In the talk the differences between classical neural networks and neural circuits in the brain will be presented. Recent hardware implementations of neuromorphic computing systems and their applications will be shown. Finally, some initial ideas to use accelerated neural architectures as trigger processors in particle physics will be discussed..

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