Name
Abstract | ||
|---|---|---|
The emergence of nanophotonic devices has enabled to design light-speed on-chip optical circuits with extremely low latency. This paper proposes an optical implementation of scalable Deep Neural Networks (DNNs) enabling light-speed inference. The key issue in optical neural networks is the scalability limited by area, power and the number of available wavelengths. Due to the scalability, it is thus difficult to design an all-optical hardware accelerator for a large-scale DNN. To solve this problem, this paper firstly proposes an optical Vector Matrix Multiplier (VMM) structure operating with a low latency. The multipliers in a VMM are highly parallelized based on the Wavelength Division Multiplexing (WDM) technique, which reduces the area overhead without sacrificing the ultra-high speed nature. This paper then proposes the electrical digital interfaces for storing and handling intermediate VMM data without sacrificing the ultra-high speed nature, which enables to reuse the VMM multiple times with a low latency. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICRC2020.2020.00017 | 2020 International Conference on Rebooting Computing (ICRC) |
Keywords | DocType | ISBN |
optical computing,deep neural network,integrated nanophotonics | Conference | 978-1-6654-1976-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Shiomi | 1 | 0 | 0.68 |
| Tohru Ishihara | 2 | 0 | 2.37 |
| Hidetoshi Onodera | 3 | 455 | 105.29 |
| Akihiko Shinya | 4 | 0 | 0.68 |
| Masaya Notomi | 5 | 9 | 9.31 |