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http://hdl.handle.net/1942/37913
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DC Field | Value | Language |
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dc.contributor.author | LI, Bowen | - |
dc.contributor.author | Xiong, Dongliang | - |
dc.contributor.author | Huang, Kai | - |
dc.contributor.author | Jiang, Xiaowen | - |
dc.contributor.author | Yao, Hao | - |
dc.contributor.author | Chen , Junjian | - |
dc.contributor.author | CLAESEN, Luc | - |
dc.date.accessioned | 2022-08-18T14:09:22Z | - |
dc.date.available | 2022-08-18T14:09:22Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-08-16T11:14:14Z | - |
dc.identifier.citation | IEICE Electronics Express, 19 (16), p. 1-6 | - |
dc.identifier.issn | 1349-2543 | - |
dc.identifier.uri | http://hdl.handle.net/1942/37913 | - |
dc.description.abstract | Quantization is a well-known method for deep neural networks (DNNs) compression and acceleration. In this work, we propose the Sample-Wise Dynamic Precision (SWDP) quantization scheme, which can switch the bit-width of weights and activations in the model according to the task difficulty of input samples at runtime. Using low-precision networks for easy input images brings advantages in terms of computational and energy efficiency. We also propose an adaptive hardware design for the efficient implementation of our SWDP networks. The experimental results on various networks and datasets demonstrate that our SWDP achieves an average of 3.3x speedup and 3.0x energy saving over the bit-level dynamically composable architecture BitFusion. | - |
dc.description.sponsorship | This work is supported by the National Key R&D Program of China (2020YFB0906000, 2020YFB0906001). | - |
dc.language.iso | en | - |
dc.publisher | IEICE-INST ELECTRONICS INFORMATION COMMUNICATION ENGINEERS | - |
dc.rights | © 2019 The Institute of Electronics, Information and Communication Engineer | - |
dc.subject.other | convolutional neural networks | - |
dc.subject.other | dynamic quantization | - |
dc.subject.other | hardware accelerators | - |
dc.title | Sample-Wise Dynamic Precision Quantization for Neural Network Acceleration | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 6 | - |
dc.identifier.issue | 16 | - |
dc.identifier.spage | 1 | - |
dc.identifier.volume | 19 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Xiong, DL (corresponding author), Zhejiang Univ, Sch Micronano Elect, Hangzhou 310030, Peoples R China. | - |
dc.description.notes | xiongdl@zju.edu.cn | - |
local.publisher.place | KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 20220229 | - |
dc.identifier.doi | 10.1587/elex.19.20220229 | - |
dc.identifier.isi | 000826303400001 | - |
dc.identifier.eissn | 1349-2543 | - |
dc.identifier.eissn | 1349-2543 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Li, Bowen; Xiong, Dongliang; Huang, Kai; Jiang, Xiaowen] Zhejiang Univ, Sch Micronano Elect, Hangzhou 310030, Peoples R China. | - |
local.description.affiliation | [Yao, Hao; Chen, Junjian] China Southern Power Grid, Digital Grid Res Inst, Guangzhou 510670, Peoples R China. | - |
local.description.affiliation | [Claesen, Luc] Univ Hasselt, Engn Technol Elect ICT Dept, Diepenbeek, Belgium. | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2023 | - |
item.accessRights | Open Access | - |
item.fullcitation | LI, Bowen; Xiong, Dongliang; Huang, Kai; Jiang, Xiaowen; Yao, Hao; Chen , Junjian & CLAESEN, Luc (2022) Sample-Wise Dynamic Precision Quantization for Neural Network Acceleration. In: IEICE Electronics Express, 19 (16), p. 1-6. | - |
item.fulltext | With Fulltext | - |
item.contributor | LI, Bowen | - |
item.contributor | Xiong, Dongliang | - |
item.contributor | Huang, Kai | - |
item.contributor | Jiang, Xiaowen | - |
item.contributor | Yao, Hao | - |
item.contributor | Chen , Junjian | - |
item.contributor | CLAESEN, Luc | - |
crisitem.journal.issn | 1349-2543 | - |
crisitem.journal.eissn | 1349-2543 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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19_19.20220229.pdf | Published version | 2.68 MB | Adobe PDF | View/Open |
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