Philipp Spilger
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Scalable network emulation on analog neuromorphic hardware Open
We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks an…
Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware Open
The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times…
Multi-timescale synaptic plasticity on analog neuromorphic hardware Open
As numerical simulations grow in complexity, their demands on computing time and energy increase. Hardware accelerators offer significant efficiency gains in many computationally intensive scientific fields, but their use in computational …
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware Open
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. While machine learning frameworks are…
Scalable Network Emulation on Analog Neuromorphic Hardware Open
We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks an…
Emulating insect brains for neuromorphic navigation Open
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment. Neurobiological imaging studies have revealed that this capability emerges from a path integration mechanism implemente…
The BrainScaleS-2 Neuromorphic Platform — A Report on the Integration and Operation of an Open Science Hardware Platform within EBRAINS Open
This report presents the challenges encountered and the solutions created for the operation of the BrainScaleS neuromorphic platform, and the overall progress leading to this state at the end of the Human Brain Project (HBP).
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware Open
A fruitful approach towards neuromorphic computing is to mimic mechanisms of the brain in physical devices, \nwhich has led to successful replication of neuronlike dynamics and learning in the past. However, there remains a \nlarge set of …
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2 Open
Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-…
Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication Open
Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear dem…
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2 Open
Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-…
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware Open
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 autoco…
Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication Open
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. The BER 2e-3 is achieved with a hardware penalty less than 1 dB, outpe…
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware Open
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 Br…
Spiking Neural Network Equalization for IM/DD Optical Communication Open
A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link. The SNN achieves the same bit-error-rate as an artificial neural network, outperforming linear equalization.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware Open
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 Br…
View article: Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System Open
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. The analog network core of the ASIC is uti…
View article: Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System Open
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. The analog network core of the ASIC is uti…
Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate Open
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 acceleratio…
Inference with Artificial Neural Networks on the Analog BrainScaleS-2 Hardware Open
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…
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware Open
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine l…
Extending BrainScaleS OS for BrainScaleS-2 Open
BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. To augment its flexibility, the analog neural network core is accompanied b…