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pro vyhledávání: '"Cartiglia, Matteo"'
Autor:
Narayanan, Shyam, Cartiglia, Matteo, Rubino, Arianna, Lego, Charles, Frenkel, Charlotte, Indiveri, Giacomo
Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural n
Externí odkaz:
http://arxiv.org/abs/2309.03221
Publikováno v:
2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processing sensory data on-line in cont
Externí odkaz:
http://arxiv.org/abs/2307.06084
Autor:
Khacef, Lyes, Klein, Philipp, Cartiglia, Matteo, Rubino, Arianna, Indiveri, Giacomo, Chicca, Elisabetta
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learn
Externí odkaz:
http://arxiv.org/abs/2209.15536
Autor:
Cartiglia, Matteo, Rubino, Arianna, Narayanan, Shyam, Frenkel, Charlotte, Haessig, Germain, Indiveri, Giacomo, Payvand, Melika
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip online learning provides such systems the ability to learn the statistic
Externí odkaz:
http://arxiv.org/abs/2201.10409
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. However, the overhead, in terms of size, complexity and power budget,
Externí odkaz:
http://arxiv.org/abs/2106.00687
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for
Externí odkaz:
http://arxiv.org/abs/2104.05241
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