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Found 8 papers, 1 papers with code
Transductive Spiking Graph Neural Networks for Loihi
We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons.
Benchmarking the human brain against computational architectures
The human brain has inspired novel concepts complementary to classical and quantum computing architectures, such as artificial neural networks and neuromorphic computers, but it is not clear how their performances compare.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Biological connectomes as a representation for the architecture of artificial neural networks
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster.
Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron
They are expected to be indispensable for energy-efficient computing in the future.
Neuromorphic Computing is Turing-Complete
Given that the {\mu}-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister.
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers
Current Deep Learning approaches have been very successful using convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers.
