dc.citation.conferencePlace |
US |
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dc.citation.conferencePlace |
New YorkNYUnited States |
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dc.citation.endPage |
152 |
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dc.citation.startPage |
138 |
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dc.citation.title |
ACM Conference on Embedded Networked Sensor Systems |
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dc.contributor.author |
Lee, Seulki |
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dc.contributor.author |
Nirjon, Shahriar |
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dc.date.accessioned |
2024-01-31T23:35:54Z |
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dc.date.available |
2024-01-31T23:35:54Z |
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dc.date.created |
2021-08-23 |
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dc.date.issued |
2019-11-11 |
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dc.description.abstract |
We introduce Neuro.ZERO-a co-processor architecture consisting of a main microcontroller (MCU) that executes scaled-down versions of a deep neural network1 (DNN) inference task, and an accelerator microcontroller that is powered by harvested energy and follows the intermittent computing paradigm [76]. The goal of the accelerator is to enhance the inference performance of the DNN that is running on the main microcontroller. Neuro.ZERO opportunistically accelerates the run-time performance of a DNN via one of its four acceleration modes: extended inference, expedited inference, ensemble inference, and latent training. To enable these modes, we propose two sets of algorithms: (1) energy and intermittence-aware DNN inference and training algorithms, and (2) a fast and high-precision adaptive fixed-point arithmetic that beats existing floating-point and fixed-point arithmetic in terms of speed and precision, respectively, and achieves the best of both. To evaluate Neuro.ZERO, we implement low-power image and audio recognition applications and demonstrate that their inference speedup increases by 1.6× and 1.7×, respectively, and the inference accuracy increases by 10% and 16%, respectively, when compared to battery-powered single-MCU systems. |
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dc.identifier.bibliographicCitation |
ACM Conference on Embedded Networked Sensor Systems, pp.138 - 152 |
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dc.identifier.doi |
10.1145/3356250.3360030 |
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dc.identifier.scopusid |
2-s2.0-85076594060 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/78870 |
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dc.language |
영어 |
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dc.publisher |
Association for Computing Machinery |
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dc.title |
Neuro.ZERO: A zero-energy neural network accelerator for embedded sensing and inference systems |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2019-11-10 |
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