Search Results for author: Sebastian Billaudelle

Found 17 papers, 1 papers with code

An accurate and flexible analog emulation of AdEx neuron dynamics in silicon

no code implementations19 Sep 2022 Sebastian Billaudelle, Johannes Weis, Philipp Dauer, Johannes Schemmel

Analog neuromorphic hardware promises fast brain emulation on the one hand and an efficient implementation of novel, brain-inspired computing paradigms on the other.

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity

no code implementations26 Jan 2022 Christian Pehle, Sebastian Billaudelle, Benjamin Cramer, Jakob Kaiser, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Aron Leibfried, Eric Müller, Johannes Schemmel

Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives.

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

no code implementations29 Mar 2021 Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser, Falk Leonard Ebert, Arne Emmel, Dan Husmann, Joscha Ilmberger, Eric Müller, Philipp Spilger, Johannes Weis, Johannes Schemmel

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset.

Total Energy

Surrogate gradients for analog neuromorphic computing

no code implementations12 Jun 2020 Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke

To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum but communicate with spikes, binary events in time.

The Operating System of the Neuromorphic BrainScaleS-1 System

no code implementations30 Mar 2020 Eric Müller, Sebastian Schmitt, Christian Mauch, Sebastian Billaudelle, Andreas Grübl, Maurice Güttler, Dan Husmann, Joscha Ilmberger, Sebastian Jeltsch, Jakob Kaiser, Johann Klähn, Mitja Kleider, Christoph Koke, José Montes, Paul Müller, Johannes Partzsch, Felix Passenberg, Hartmut Schmidt, Bernhard Vogginger, Jonas Weidner, Christian Mayr, Johannes Schemmel

We present operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture and walk through the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation.

Accelerated Analog Neuromorphic Computing

no code implementations26 Mar 2020 Johannes Schemmel, Sebastian Billaudelle, Phillip Dauer, Johannes Weis

The presented architecture is based upon a continuous-time, analog, physical model implementation of neurons and synapses, resembling an analog neuromorphic accelerator attached to build-in digital compute cores.

Verification and Design Methods for the BrainScaleS Neuromorphic Hardware System

no code implementations25 Mar 2020 Andreas Grübl, Sebastian Billaudelle, Benjamin Cramer, Vitali Karasenko, Johannes Schemmel

This paper presents verification and implementation methods that have been developed for the design of the BrainScaleS-2 65nm ASICs.

Structural plasticity on an accelerated analog neuromorphic hardware system

no code implementations27 Dec 2019 Sebastian Billaudelle, Benjamin Cramer, Mihai A. Petrovici, Korbinian Schreiber, David Kappel, Johannes Schemmel, Karlheinz Meier

In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

no code implementations8 Nov 2018 Timo Wunderlich, Akos F. Kungl, Eric Müller, Andreas Hartel, Yannik Stradmann, Syed Ahmed Aamir, Andreas Grübl, Arthur Heimbrecht, Korbinian Schreiber, David Stöckel, Christian Pehle, Sebastian Billaudelle, Gerd Kiene, Christian Mauch, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency.

Porting HTM Models to the Heidelberg Neuromorphic Computing Platform

no code implementations8 May 2015 Sebastian Billaudelle, Subutai Ahmad

Hierarchical Temporal Memory (HTM) is a computational theory of machine intelligence based on a detailed study of the neocortex.

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