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001     1038223
005     20250203103321.0
024 7 _ |a 10.48550/ARXIV.2408.15800
|2 doi
037 _ _ |a FZJ-2025-01259
100 1 _ |a Stewart, Kenneth
|0 P:(DE-Juel1)195754
|b 0
245 _ _ |a Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing
260 _ _ |c 2024
|b arXiv
336 7 _ |a Preprint
|b preprint
|m preprint
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|s 1738573093_11958
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336 7 _ |a WORKING_PAPER
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336 7 _ |a Electronic Article
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336 7 _ |a preprint
|2 DRIVER
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a Output Types/Working Paper
|2 DataCite
520 _ _ |a Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement.
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Neural and Evolutionary Computing (cs.NE)
|2 Other
650 _ 7 |a Artificial Intelligence (cs.AI)
|2 Other
650 _ 7 |a FOS: Computer and information sciences
|2 Other
700 1 _ |a Neumeier, Michael
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Shrestha, Sumit Bam
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Orchard, Garrick
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Neftci, Emre
|0 P:(DE-Juel1)188273
|b 4
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773 _ _ |a 10.48550/ARXIV.2408.15800
909 C O |p VDB
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
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|l Natural, Artificial and Cognitive Information Processing
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|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5234
|x 0
914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)PGI-15-20210701
|k PGI-15
|l Neuromorphic Software Eco System
|x 0
980 _ _ |a preprint
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)PGI-15-20210701
980 _ _ |a UNRESTRICTED

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