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Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Yigit Demirag, Charlotte Frenkel, Melika Payvand +1
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse proces…
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
Charlotte Frenkel, David Bol, Giacomo Indiveri
While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is t…
Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
Nik Dennler, Germain Haessig, Matteo Cartiglia +1
Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems.…
Introducing "Neuromorphic Computing and Engineering"
Giacomo Indiveri
The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address s…
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…
Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
Giorgia Dellaferrera, Stanislaw Wozniak, Giacomo Indiveri +2
Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synap…