Spiking Neural Networks
Spiking Neural Networks
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Spiking Neural Networks (SNNs) are a type of artificial neural network that mimics the way biological neurons work by incorporating a time-dependent behavior into the neurons. This behavior allows neurons to communicate with each other and process information in a more biologically-realistic way. Instead of relying solely on activation values like traditional neural networks, SNNs use spikes, or electrical impulses, to send and receive information. This makes them better at modeling complex, dynamic relationships and can help improve the performance of machine learning applications.
... The core of our technology is spiking neural networks ...
... NeuroCAD - Design Software for Spiking Neural Networks ...
... the Design, Automation and Test in Europe (DATE 2023) Conference titled ‘Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization’ and ‘Security-Aware Approximate Spiking Neural Network’ with an acceptance rate of 25%. See Preprint 1, Preprint 2, ...
... The future products in the Edge IoT domain stand on HW/SW platforms based on efficient Neuromorphic, artificial and spiking neural networks, ...
... Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks Anonymous reviewerAnonymous 100 89 ...
... "Hardware approximation of exponential decay for spiking neural networks". IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington DC, DC, USA: 1-5 (2021). https://doi.org/10.1109/AICAS51828.2021.9458560 Hilgenkamp, H. and X. Gao. "Exploring the ...
... "Hardware approximation of exponential decay for spiking neural networks". IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington DC, DC, USA: 1-5 (2021). https://doi.org/10.1109/AICAS51828.2021.9458560 Hilgenkamp, H. and X. Gao. "Exploring the ...
... Using novel models such as Spiking Neural Networks (SNNs), we can take into account the temporal aspect of incoming streams of data. ...
... Our processors have hybrid implementation of deep neural networks, spiking neural networks, and reinforcement learning that gives adaptable learning capability directly on the hardware. So, our flexible AI processor improves safety of autonomous vehicles, makes service/industrial robots more ...
... Exploring Applications of Spiking Neural Networks ...
... Collectively, we have knowledge in Mathematics and Physics, Computer Science, Quantitative Finance and Trading, Neuroscience, Spiking Neural Networks, Deep Learning, Optimization, Statistics, Network Science, Recommender Systems, DevOps, Software and Machine Learning Engineering and more. ...