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jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware
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.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
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.
Emulating insect brains for neuromorphic navigation
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment.
Spiking Neural Network Nonlinear Demapping on Neuromorphic Hardware for IM/DD Optical Communication
The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2).
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
Neuromorphic systems require user-friendly software to support the design and optimization of experiments.
Autocorrelations from emergent bistability in homeostatic spiking neural networks on neuromorphic hardware
The extent of memory about past inputs is commonly quantified by the autocorrelation time of collective dynamics.
Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication
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
A spiking neural network (SNN) equalizer model suitable for electronic neuromorphic hardware is designed for an IM/DD link.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research.
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
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.
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and synapse circuits as well as two versatile digital microprocessors.
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks.
Extending BrainScaleS OS for BrainScaleS-2
BrainScaleS-2 is a mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing.
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
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.
