Title |
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A study on a low power optimization algorithm for an edge-AI device
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Creator |
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Accessrights |
metadata only access |
Rights |
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Copyright ©2019 The Institute of Electronics, Information and Communication Engineers
- https://search.ieice.org/
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https://search.ieice.org/
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Subject |
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Other
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machine learning
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Other
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edge AI
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training algorithm
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backpropagation
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quantization
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Other
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low power
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NDC
547
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Description |
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Abstract
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Although research on the inference phase of edge artificial intelligence (AI) has made considerable improvement, the required training phase remains an unsolved problem. Neural network (NN) processing has two phases: inference and training. In the training phase, a NN incurs high calculation cost. The number of bits (bitwidth) in the training phase is several orders of magnitude larger than that in the inference phase. Training algorithms, optimized to software, are not appropriate for training hardware-oriented NNs. Therefore, we propose a new training algorithm for edge AI: backpropagation (BP) using a ternarized gradient. This ternarized backpropagation (TBP) provides a balance between calculation cost and performance. Empirical results demonstrate that in a two-class classification task, TBP works well in practice and compares favorably with 16-bit BP (Fixed-BP).
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Publisher |
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電子情報通信学会(The Institute of Electronics, Information and Communication Engineers / IEICE)
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Date |
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Resource Type |
journal article |
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NA |
Identifier |
HDL
http://hdl.handle.net/2115/76016
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DOI
https://doi.org/10.1587/nolta.10.373
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Journal |
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Nonlinear theory and its applications, IEICE
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Volume Number10
Issue Number4
Page Start373
Page End389
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Oaidate |
2023-07-26 |