DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Yoonho | - |
dc.contributor.author | Kang, Yesung | - |
dc.contributor.author | Kim, Sunghoon | - |
dc.contributor.author | Kwon, Eunji | - |
dc.contributor.author | Kang, Seokhyeong | - |
dc.date.accessioned | 2021-06-01T08:04:46Z | - |
dc.date.available | 2021-06-01T08:04:46Z | - |
dc.date.created | 2021-03-10 | - |
dc.date.issued | 2020-08 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/106075 | - |
dc.description.abstract | Convolutional neural networks (CNNs) require a huge amount of off-chip DRAM access, which accounts for most of its energy consumption. Compression of feature maps can reduce the energy consumption of DRAM access. However, previous compression methods show poor compression ratio if the feature maps are either extremely sparse or dense. To improve the compression ratio efficiently, we have exploited the spatial correlation and the distribution of non-zero activations in output feature maps. In this work, we propose a grid-based run-length compression (GRLC) and have implemented a hardware for the GRLC. Compared with a previous compression method [1], GRLC reduces 11% of the DRAM access and 5% of the energy consumption on average in VGG-16, ExtractionNet and ResNet-18. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.relation.isPartOf | 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.title | GRLC: Grid-based run-length compression for energy-efficient CNN accelerator | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.identifier.bibliographicCitation | 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 | - |
dc.citation.conferenceDate | 2020-08-10 | - |
dc.citation.conferencePlace | US | - |
dc.citation.title | 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 | - |
dc.contributor.affiliatedAuthor | Kang, Seokhyeong | - |
dc.identifier.scopusid | 2-s2.0-85098243831 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.