162 citations · 783 across the 23 of their papers we have counts for
23 papers
Neuromorphic hardware for sustainable AI data centers
Bernhard Vogginger, Amirhossein Rostami, Vaibhav Jain +9
As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solut…
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
Philipp Plank, Arjun Rao, Andreas Wild +1
Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this re…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
Luke Y. Prince, Roy Henha Eyono, Ellen Boven +9
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based l…
Embodied Synaptic Plasticity with Online Reinforcement learning
Jacques Kaiser, Michael Hoff, Andreas Konle +8
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in…
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes
Christoph Stöckl, Wolfgang Maass
Spike-based neuromorphic hardware promises to reduce the energy consumption of image classification and other deep learning applications, particularly on mobile phones or other edg…