ID | 118049 |
Author |
Nsinga, Robert
Tokushima University
Karungaru, Stephen
Tokushima University
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Terada, Kenji
Tokushima University
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Keywords | embedded system
IEEE 754-2008 floating-point
digital signal processor
Q format notation
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Content Type |
Journal Article
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Description | Using less electric power or speeding up processing is catching the interests of researchers in deep learning. Quantization has offered distillation mechanisms that substitute floating numbers for integers, but little has been suggested about the floating numbers themselves. The use of Q-format notation reduces computational overheads that frees resources for the introduction of more operations. Our experiments, conditioned on varying regimes, introduce automatic differentiation on algorithms like the fast Fourier transforms and Winograd minimal filtering to reduce computational complexity (expressed in total number of MACs) and suggest a path towards the assistive intelligence concept. Empirical results show that, under specific heuristics, the Q-format number notation can overcome the shortfalls of floating numbers, especially for embedded systems. Further benchmarks like the FPBench standard give more details by comparing our proposals with common deep learning operations.
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Journal Title |
Journal of Signal Processing
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ISSN | 18801013
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Publisher | Research Institute of Signal Processing
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Volume | 26
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Issue | 5
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Start Page | 131
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End Page | 140
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Published Date | 2022-09-01
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Remark | 利用は著作権の範囲内に限られる。
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
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language |
eng
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TextVersion |
Publisher
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departments |
Science and Technology
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