Training Neural Networks for Execution on Approximate Hardware Article Swipe
Tianmu Li
,
Shurui Li
,
Puneet Gupta
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.04125
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.04125
Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this work, we discuss training methods for approximate hardware. We demonstrate how training needs to be specialized for approximate hardware, and propose methods to speed up the training process by up to 18X.
Related Topics
Concepts
Computer science
Inference
Training (meteorology)
Work (physics)
Process (computing)
Artificial neural network
Power (physics)
Computer engineering
Computer hardware
Deep neural networks
Artificial intelligence
Machine learning
Embedded system
Operating system
Physics
Mechanical engineering
Meteorology
Engineering
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.04125
- https://arxiv.org/pdf/2304.04125
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4364383148
All OpenAlex metadata
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https://openalex.org/W4364383148Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.04125Digital Object Identifier
- Title
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Training Neural Networks for Execution on Approximate HardwareWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-04-08Full publication date if available
- Authors
-
Tianmu Li, Shurui Li, Puneet GuptaList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.04125Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.04125Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2304.04125Direct OA link when available
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Computer science, Inference, Training (meteorology), Work (physics), Process (computing), Artificial neural network, Power (physics), Computer engineering, Computer hardware, Deep neural networks, Artificial intelligence, Machine learning, Embedded system, Operating system, Physics, Mechanical engineering, Meteorology, Engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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