Search Results for author: Johannes Schemmel

Found 50 papers, 6 papers with code

Lu.i -- A low-cost electronic neuron for education and outreach

1 code implementation25 Apr 2024 Yannik Stradmann, Julian Göltz, Mihai A. Petrovici, Johannes Schemmel, Sebastian Billaudelle

With an increasing presence of science throughout all parts of society, there is a rising expectation for researchers to effectively communicate their work and, equally, for teachers to discuss contemporary findings in their classrooms.

Towards Large-scale Network Emulation on Analog Neuromorphic Hardware

no code implementations30 Jan 2024 Elias Arnold, Philipp Spilger, Jan V. Straub, Eric Müller, Dominik Dold, Gabriele Meoni, Johannes Schemmel

We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.

jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware

no code implementations30 Jan 2024 Eric Müller, Moritz Althaus, Elias Arnold, Philipp Spilger, Christian Pehle, Johannes Schemmel

Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons.

Gradient-based methods for spiking physical systems

no code implementations29 Aug 2023 Julian Göltz, Sebastian Billaudelle, Laura Kriener, Luca Blessing, Christian Pehle, Eric Müller, Johannes Schemmel, Mihai A. Petrovici

Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico.

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 Apr 2023 Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.


Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware

1 code implementation28 Mar 2023 Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt

The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons.

Event-based Backpropagation for Analog Neuromorphic Hardware

no code implementations13 Feb 2023 Christian Pehle, Luca Blessing, Elias Arnold, Eric Müller, Johannes Schemmel

Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances.

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.

Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication

no code implementations1 Jun 2022 Elias Arnold, Georg Böcherer, Eric Müller, Philipp Spilger, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov

A spiking neural network (SNN) non-linear equalizer model is implemented on the mixed-signal neuromorphic hardware system BrainScaleS-2 and evaluated for an IM/DD link.

Spiking Neural Network Equalization for IM/DD Optical Communication

no code implementations9 May 2022 Elias Arnold, Georg Böcherer, Eric Müller, Philipp Spilger, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov

A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link.

Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL

no code implementations24 Feb 2022 Tobias Thommes, Sven Bordukat, Andreas Grübl, Vitali Karasenko, Eric Müller, Johannes Schemmel

The BrainScaleS-2 (BSS-2) Neuromorphic Computing System currently consists of multiple single-chip setups, which are connected to a compute cluster via Gigabit-Ethernet network technology.

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.

BrainScaleS Large Scale Spike Communication using Extoll

no code implementations30 Nov 2021 Tobias Thommes, Niels Buwen, Andreas Grübl, Eric Müller, Ulrich Brüning, Johannes Schemmel

The BrainScaleS Neuromorphic Computing System is currently connected to a compute cluster via Gigabit-Ethernet network technology.

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.

Extending BrainScaleS OS for BrainScaleS-2

no code implementations30 Mar 2020 Eric Müller, Christian Mauch, Philipp Spilger, Oliver Julien Breitwieser, Johann Klähn, David Stöckel, Timo Wunderlich, Johannes Schemmel

BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing.

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.

Computational Efficiency

Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network

no code implementations24 Sep 2019 Timo C. Wunderlich, Akos F. Kungl, Eric Müller, Johannes Schemmel, Mihai Petrovici

Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing.

Reinforcement Learning (RL)

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.

Stochasticity from function -- why the Bayesian brain may need no noise

no code implementations21 Sep 2018 Dominik Dold, Ilja Bytschok, Akos F. Kungl, Andreas Baumbach, Oliver Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.

Bayesian Inference

Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain

no code implementations23 May 2018 Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.

Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster

no code implementations15 Jan 2018 Kai Zoschke, Maurice Güttler, Lars Böttcher, Andreas Grübl, Dan Husmann, Johannes Schemmel, Karlheinz Meier, Oswin Ehrmann

Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has developed a full wafer redistribution and embedding technology as base for a large-scale neuromorphic hardware system.

Spiking neurons with short-term synaptic plasticity form superior generative networks

no code implementations24 Sep 2017 Luziwei Leng, Roman Martel, Oliver Breitwieser, Ilja Bytschok, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.

Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware

no code implementations12 Mar 2017 Mihai A. Petrovici, Anna Schroeder, Oliver Breitwieser, Andreas Grübl, Johannes Schemmel, Karlheinz Meier

How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferring these computational principles to neuromorphic silicon circuits.

Stochastic inference with spiking neurons in the high-conductance state

no code implementations23 Oct 2016 Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro.

Bayesian Inference Vocal Bursts Intensity Prediction

Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System

no code implementations18 Apr 2016 Simon Friedmann, Johannes Schemmel, Andreas Gruebl, Andreas Hartel, Matthias Hock, Karlheinz Meier

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.

The high-conductance state enables neural sampling in networks of LIF neurons

no code implementations5 Jan 2016 Mihai A. Petrovici, Ilja Bytschok, Johannes Bill, Johannes Schemmel, Karlheinz Meier

The core idea of our approach is to separately consider two different "modes" of spiking dynamics: burst spiking and transient quiescence, in which the neuron does not spike for longer periods.

Bayesian Inference

Stochastic inference with deterministic spiking neurons

no code implementations13 Nov 2013 Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, Karlheinz Meier

The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference.

Bayesian Inference

A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model

no code implementations NeurIPS 2010 Sebastian Millner, Andreas Grübl, Karlheinz Meier, Johannes Schemmel, Marc-Olivier Schwartz

We describe an accelerated hardware neuron being capable of emulating the adap-tive exponential integrate-and-fire neuron model.

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