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cs.NE2024

Short-reach Optical Communications: A Real-world Task for Neuromorphic Hardware

Elias Arnold, Eike-Manuel Edelmann, Alexander von Bank +3

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditi…

cs.NE2024

Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware

Philipp Spilger, Eric Müller, Johannes Schemmel

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…

cs.NE2024

Demonstrating the Advantages of Analog Wafer-Scale Neuromorphic Hardware

Hartmut Schmidt, Andreas Grübl, José Montes +3

As numerical simulations grow in size and complexity, they become increasingly resource-intensive in terms of time and energy. While specialized hardware accelerators often provide…

cs.NE2024

Reproduction of AdEx dynamics on neuromorphic hardware through data embedding and simulation-based inference

Jakob Huhle, Jakob Kaiser, Eric Müller +1

The development of mechanistic models of physical systems is essential for understanding their behavior and formulating predictions that can be validated experimentally. Calibratio…

cs.NE2024

Scalable Network Emulation on Analog Neuromorphic Hardware

Elias Arnold, Philipp Spilger, Jan V. Straub +4

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…

cs.NE2024

jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware

Eric Müller, Moritz Althaus, Elias Arnold +3

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