期刊
SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-022-08703-1
关键词
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资金
- UKRI Turing AI Acceleration Fellowships Programme [EP/V025198/1]
- US Office of Naval Research Global [ONRG-NICOP-N62909-18-1-2027]
- European Commission [828841-ChipAI-H2020-FETOPEN-2018-2020]
- UK EPSRC [EP/N509760/1, EP/P006973/1]
- Leonardo UK Ltd through the Leonardo Lectureship at Strathclyde
- EPSRC [EP/V025198/1] Funding Source: UKRI
The hardware-friendly neuromorphic photonic processor utilizing VCSEL technology enables high-speed all-optical image edge-feature detection. By integrating with a software-implemented spiking neural network, it provides a full platform for complex image classification tasks. This work highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems.
The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.
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