| dc.contributor.author | Chen, Yu-Hsin | |
| dc.contributor.author | Emer, Joel S | |
| dc.contributor.author | Sze, Vivienne | |
| dc.date.accessioned | 2021-03-08T22:07:31Z | |
| dc.date.available | 2021-03-08T22:07:31Z | |
| dc.date.issued | 2017-06 | |
| dc.identifier.issn | 0272-1732 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/130106 | |
| dc.description.abstract | The authors demonstrate the key role dataflows play in the optimization of energy efficiency for deep neural network (DNN) accelerators. By introducing a systematic approach to analyze the problem and a new dataflow, called Row-Stationary, which is up to 2.5 times more energy efficient than existing dataflows in processing a state-of-the-art DNN, this work provides guidelines for future DNN accelerator designs. | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1109/mm.2017.54 | en_US |
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
| dc.source | Prof. Sze via Phoebe Ayers | en_US |
| dc.title | Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Chen, Yu-Hsin et al. "Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators." IEEE Micro 37, 3 (June 2017): 12 - 21. © 2017 IEEE | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Microsystems Technology Laboratories | en_US |
| dc.relation.journal | IEEE Micro | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dspace.date.submission | 2021-03-05T15:04:35Z | |
| mit.journal.volume | 37 | en_US |
| mit.journal.issue | 3 | en_US |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Complete | |
