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  1. Book ; Online: Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL

    Thommes, Tobias / Bordukat, Sven / Grübl, Andreas / Karasenko, Vitali / Müller, Eric / Schemmel, Johannes

    2022  

    Abstract: 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. This is convenient for small experiments, where the neural ... ...

    Abstract 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. This is convenient for small experiments, where the neural networks fit into a single chip. When modeling networks of larger size, neurons have to be connected across chip boundaries. We implement these connections for BSS-2 using the EXTOLL networking technology. This provides high bandwidths and low latencies, as well as high message rates. Here, we describe the targeted pulse-routing implementation and required extensions to the BSS-2 software stack. We as well demonstrate feed-forward pulse-routing on BSS-2 using a scaled-down version without temporal merging.

    Comment: 3 pages, 2 figures, submitted to the Neuro Inspired Computational Elements 2022 (NICE'2022) conference, accepted and presented as a lightning-talk in March 2022; 1st replacement: version to be published in the conference proceedings
    Keywords Computer Science - Hardware Architecture ; Computer Science - Neural and Evolutionary Computing ; Computer Science - Networking and Internet Architecture
    Publishing date 2022-02-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Surrogate gradients for analog neuromorphic computing.

    Cramer, Benjamin / Billaudelle, Sebastian / Kanya, Simeon / Leibfried, Aron / Grübl, Andreas / Karasenko, Vitali / Pehle, Christian / Schreiber, Korbinian / Stradmann, Yannik / Weis, Johannes / Schemmel, Johannes / Zenke, Friedemann

    Proceedings of the National Academy of Sciences of the United States of America

    2022  Volume 119, Issue 4

    Abstract: 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. Analog neuromorphic hardware uses the same principles to emulate spiking neural ... ...

    Abstract 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. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.
    MeSH term(s) Action Potentials/physiology ; Algorithms ; Brain/physiology ; Computers ; Models, Biological ; Models, Neurological ; Models, Theoretical ; Neural Networks, Computer ; Neurons/physiology
    Language English
    Publishing date 2022-01-18
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2109194119
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  3. Book ; Online: Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware

    Cramer, Benjamin / Kreft, Markus / Billaudelle, Sebastian / Karasenko, Vitali / Leibfried, Aron / Müller, Eric / Spilger, Philipp / Weis, Johannes / Schemmel, Johannes / Muñoz, Miguel A. / Priesemann, Viola / Zierenberg, Johannes

    2022  

    Abstract: A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of memory about past inputs is commonly quantified by the autocorrelation time ... ...

    Abstract A unique feature of neuromorphic computing is that memory is an implicit part of processing through traces of past information in the system's collective dynamics. The extent of memory about past inputs is commonly quantified by the autocorrelation time of collective dynamics. Based on past experimental evidence, a potential explanation for the underlying autocorrelations are close-to-critical fluctuations. Here, we show for self-organized networks of excitatory and inhibitory leaky integrate-and-fire neurons that autocorrelations can originate from emergent bistability upon reducing external input strength. We identify the bistability as a fluctuation-induced stochastic switching between metastable active and quiescent states in the vicinity of a non-equilibrium phase transition. This bistability occurs for networks with fixed heterogeneous weights as a consequence of homeostatic self-organization during development. Specifically, in our experiments on neuromorphic hardware and in computer simulations, the emergent bistability gives rise to autocorrelation times exceeding 500 ms despite single-neuron timescales of only 20 ms. Our results provide the first verification of biologically compatible autocorrelation times in networks of leaky integrate-and-fire neurons, which here are not generated by close-to-critical fluctuations but by emergent bistability in homeostatically regulated networks. Our results thereby constitute a new, complementary mechanism for emergent autocorrelations in networks of spiking neurons, with implications for biological and artificial networks, and introduces the general paradigm of fluctuation-induced bistability for driven systems with absorbing states.
    Keywords Quantitative Biology - Neurons and Cognition ; Nonlinear Sciences - Adaptation and Self-Organizing Systems
    Subject code 612
    Publishing date 2022-08-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

    Müller, Eric / Arnold, Elias / Breitwieser, Oliver / Czierlinski, Milena / Emmel, Arne / Kaiser, Jakob / Mauch, Christian / Schmitt, Sebastian / Spilger, Philipp / Stock, Raphael / Stradmann, Yannik / Weis, Johannes / Baumbach, Andreas / Billaudelle, Sebastian / Cramer, Benjamin / Ebert, Falk / Göltz, Julian / Ilmberger, Joscha / Karasenko, Vitali /
    Kleider, Mitja / Leibfried, Aron / Pehle, Christian / Schemmel, Johannes

    Frontiers in neuroscience

    2022  Volume 16, Page(s) 884128

    Abstract: Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 ... ...

    Abstract Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability, and efficiency.
    Language English
    Publishing date 2022-05-18
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2022.884128
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  5. Book ; Online: Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

    Weis, Johannes / Spilger, Philipp / Billaudelle, Sebastian / Stradmann, Yannik / Emmel, Arne / Müller, Eric / Breitwieser, Oliver / Grübl, Andreas / Ilmberger, Joscha / Karasenko, Vitali / Kleider, Mitja / Mauch, Christian / Schreiber, Korbinian / Schemmel, Johannes

    2020  

    Abstract: The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication ...

    Abstract The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors. Primarily designed to emulate spiking neural networks, the system can also operate in a vector-matrix multiplication and accumulation mode for artificial neural networks. Analog multiplication is carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors. Designed as an analog, in-memory computing device, it promises high energy efficiency. Fixed-pattern noise and trial-to-trial variations, however, require the implemented networks to cope with a certain level of perturbations. Further limitations are imposed by the digital resolution of the input values (5 bit), matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop. Among other benchmarks, we classify the MNIST handwritten digits dataset using a two-dimensional convolution and two dense layers. We reach 98.0% test accuracy, closely matching the performance of the same network evaluated in software.
    Keywords Computer Science - Neural and Evolutionary Computing
    Publishing date 2020-06-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Surrogate gradients for analog neuromorphic computing

    Cramer, Benjamin / Billaudelle, Sebastian / Kanya, Simeon / Leibfried, Aron / Grübl, Andreas / Karasenko, Vitali / Pehle, Christian / Schreiber, Korbinian / Stradmann, Yannik / Weis, Johannes / Schemmel, Johannes / Zenke, Friedemann

    2020  

    Abstract: 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. Analog neuromorphic hardware uses the same principles to emulate spiking neural ... ...

    Abstract 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. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy-efficiency. Nevertheless, emulating high-performing spiking networks on such hardware remains a significant challenge due to device-mismatch and the lack of efficient training algorithms. Here, we introduce a general in-the-loop learning framework that resolves these issues. Using the BrainScales-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on vision and speech benchmarks. Our networks display sparse spiking activity with, on average, far less than one spike per hidden neuron, perform inference at rates of up to 85 k frames/second, and consume less than 200 mW. In summary, our work sets several new benchmarks for low-energy spiking network processing on analog neuromorphic substrates and constitutes an important step toward on-chip learning.
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Emerging Technologies ; Computer Science - Machine Learning ; Quantitative Biology - Neurons and Cognition ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-06-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article: Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.

    Kungl, Akos F / Schmitt, Sebastian / Klähn, Johann / Müller, Paul / Baumbach, Andreas / Dold, Dominik / Kugele, Alexander / Müller, Eric / Koke, Christoph / Kleider, Mitja / Mauch, Christian / Breitwieser, Oliver / Leng, Luziwei / Gürtler, Nico / Güttler, Maurice / Husmann, Dan / Husmann, Kai / Hartel, Andreas / Karasenko, Vitali /
    Grübl, Andreas / Schemmel, Johannes / Meier, Karlheinz / Petrovici, Mihai A

    Frontiers in neuroscience

    2019  Volume 13, Page(s) 1201

    Abstract: The massively parallel nature of biological information processing plays an important role due to its superiority in comparison to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits ...

    Abstract The massively parallel nature of biological information processing plays an important role due to its superiority in comparison to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.
    Language English
    Publishing date 2019-11-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2019.01201
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

    Billaudelle, Sebastian / Stradmann, Yannik / Schreiber, Korbinian / Cramer, Benjamin / Baumbach, Andreas / Dold, Dominik / Göltz, Julian / Kungl, Akos F. / Wunderlich, Timo C. / Hartel, Andreas / Müller, Eric / Breitwieser, Oliver / Mauch, Christian / Kleider, Mitja / Grübl, Andreas / Stöckel, David / Pehle, Christian / Heimbrecht, Arthur / Spilger, Philipp /
    Kiene, Gerd / Karasenko, Vitali / Senn, Walter / Petrovici, Mihai A. / Schemmel, Johannes / Meier, Karlheinz

    2019  

    Abstract: We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of ... ...

    Abstract We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system.
    Keywords Quantitative Biology - Neurons and Cognition ; Computer Science - Neural and Evolutionary Computing
    Publishing date 2019-12-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Accelerated physical emulation of Bayesian inference in spiking neural networks

    Kungl, Akos F. / Schmitt, Sebastian / Klähn, Johann / Müller, Paul / Baumbach, Andreas / Dold, Dominik / Kugele, Alexander / Gürtler, Nico / Leng, Luziwei / Müller, Eric / Koke, Christoph / Kleider, Mitja / Mauch, Christian / Breitwieser, Oliver / Güttler, Maurice / Husmann, Dan / Husmann, Kai / Ilmberger, Joscha / Hartel, Andreas /
    Karasenko, Vitali / Grübl, Andreas / Schemmel, Johannes / Meier, Karlheinz / Petrovici, Mihai A.

    2018  

    Abstract: The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary ... ...

    Abstract The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.

    Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi:10.3389/fnins.2019.01201
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Emerging Technologies
    Subject code 006
    Publishing date 2018-07-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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