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- aggregation classification "A1".
- aggregation creator person.
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- aggregation date "2015".
- aggregation format "application/pdf".
- aggregation hasFormat 5912189.bibtex.
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- aggregation isPartOf urn:issn:2041-1723.
- aggregation language "eng".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Technology and Engineering".
- aggregation title "Trainable hardware for dynamical computing using error backpropagation through physical media".
- aggregation abstract "Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation—a crucial step for tuning such systems towards a specific task—can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.".
- aggregation authorList BK1158218.
- aggregation volume "6".
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