| dc.contributor.author | Bernstein, Liane | |
| dc.contributor.author | Sludds, Alexander | |
| dc.contributor.author | Hamerly, Ryan M | |
| dc.contributor.author | Sze, Vivienne | |
| dc.contributor.author | Emer, Joel S | |
| dc.contributor.author | Englund, Dirk R. | |
| dc.date.accessioned | 2021-03-08T21:52:39Z | |
| dc.date.available | 2021-03-08T21:52:39Z | |
| dc.date.issued | 2020-05 | |
| dc.identifier.isbn | 9781943580767 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/130105 | |
| dc.description.abstract | We propose a digital incoherent optical neural network architecture using the passive data routing and copying capabilities of optics for artificial neural network acceleration. We demonstrate a proof-of-concept experiment and analyze optimal use cases. | en_US |
| dc.publisher | Optical Society of America (OSA) | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1364/cleo_si.2020.sm1e.4 | 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 | Digital Optical Neural Networks for Large-Scale Machine Learning | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Bernstein, Liane et al. "Digital Optical Neural Networks for Large-Scale Machine Learning." Conference on Lasers and Electro-Optics, May 2020, Washington, D. C., Optical Society of America, May 2020. © 2020 The Author(s) | 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. Research Laboratory of Electronics | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Microsystems Technology Laboratories | en_US |
| dc.relation.journal | Conference on Lasers and Electro-Optics | en_US |
| dc.eprint.version | Author's final manuscript | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dspace.date.submission | 2021-03-05T14:58:26Z | |
| mit.license | OPEN_ACCESS_POLICY | |
| mit.metadata.status | Complete | |
