Demonstrating hybrid learning in a flexible neuromorphic hardware system

• Verfasst von: Friedmann, Simon [VerfasserIn]
• Verfasst von: Schemmel, Johannes [VerfasserIn]
• Verfasst von: Grübl, Andreas [VerfasserIn]
• Verfasst von: Hartel, Andreas [VerfasserIn]
• Verfasst von: Hock, Matthias [VerfasserIn]
• Verfasst von: Meier, Karlheinz [VerfasserIn]
• Titel: Demonstrating hybrid learning in a flexible neuromorphic hardware system
• Verf.angabe: Simon Friedmann, Johannes Schemmel, Member, IEEE, Andreas Grübl, Andreas Hartel, Matthias Hock, and Karlheinz Meier
• Umfang: 15 S.
• Fussnoten: Gesehen am 08.05.2018
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics Engineers: IEEE transactions on biomedical circuits and systems
• Jahr Quelle: 2017
• Band/Heft Quelle: 11(2017), 1, S. 128-142
• ISSN Quelle: 1940-9990
• DOI: doi:10.1109/TBCAS.2016.2579164
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1574274139

Abstract

We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.