Energy-efficient neural network inference with microcavity exciton polaritons
We propose all-optical neural networks characterized by very high energy efficiency and performance density of inference. We argue that the use of microcavity exciton-polaritons allows to take advantage of the properties of both photons and electrons in a seamless manner. This results in strong o...
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| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154197 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | We propose all-optical neural networks characterized by very high energy
efficiency and performance density of inference. We argue that the use of
microcavity exciton-polaritons allows to take advantage of the properties of
both photons and electrons in a seamless manner. This results in strong optical
nonlinearity without the use of optoelectronic conversion. We propose a design
of a realistic neural network and estimate energy cost to be at the level of
attojoules per bit, also when including the optoelectronic conversion at the
input and output of the network, several orders of magnitude below
state-of-the-art hardware implementations. We propose two kinds of nonlinear
binarized nodes based either on optical phase shifts and interferometry or on
polariton spin rotations. |
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