Deep Learning with Passive Optical Nonlinear Mapping
- URL: http://arxiv.org/abs/2307.08558v2
- Date: Tue, 18 Jul 2023 15:23:48 GMT
- Title: Deep Learning with Passive Optical Nonlinear Mapping
- Authors: Fei Xia, Kyungduk Kim, Yaniv Eliezer, Liam Shaughnessy, Sylvain Gigan,
Hui Cao
- Abstract summary: We introduce a design that leverages multiple scattering in a reverberating cavity to passively induce optical nonlinear random mapping.
We show we can perform optical data compression, facilitated by multiple scattering in the cavity, to efficiently compress and retain vital information.
Our findings pave the way for novel algorithms and architectural designs for optical computing.
- Score: 9.177212626554505
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning has fundamentally transformed artificial intelligence, but the
ever-increasing complexity in deep learning models calls for specialized
hardware accelerators. Optical accelerators can potentially offer enhanced
performance, scalability, and energy efficiency. However, achieving nonlinear
mapping, a critical component of neural networks, remains challenging
optically. Here, we introduce a design that leverages multiple scattering in a
reverberating cavity to passively induce optical nonlinear random mapping,
without the need for additional laser power. A key advantage emerging from our
work is that we show we can perform optical data compression, facilitated by
multiple scattering in the cavity, to efficiently compress and retain vital
information while also decreasing data dimensionality. This allows rapid
optical information processing and generation of low dimensional mixtures of
highly nonlinear features. These are particularly useful for applications
demanding high-speed analysis and responses such as in edge computing devices.
Utilizing rapid optical information processing capabilities, our optical
platforms could potentially offer more efficient and real-time processing
solutions for a broad range of applications. We demonstrate the efficacy of our
design in improving computational performance across tasks, including
classification, image reconstruction, key-point detection, and object
detection, all achieved through optical data compression combined with a
digital decoder. Notably, we observed high performance, at an extreme
compression ratio, for real-time pedestrian detection. Our findings pave the
way for novel algorithms and architectural designs for optical computing.
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