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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).
An accurate and flexible analog emulation of AdEx neuron dynamics in silicon
Analog neuromorphic hardware promises fast brain emulation on the one hand and an efficient implementation of novel, brain-inspired computing paradigms on the other.
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.
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research.
The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity
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
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.
Surrogate gradients for analog neuromorphic computing
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.
Accelerated Analog Neuromorphic Computing
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.
