Supervised learning and pattern recognition in photonic spiking neural networks based on MRR and phase-change materials
Optics Communications, ISSN: 0030-4018, Vol: 549, Page: 129870
2023
- 5Citations
- 7Captures
- 1Mentions
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Reports from Xidian University Add New Data to Findings in Phase Change Materials (Supervised Learning and Pattern Recognition In Photonic Spiking Neural Networks Based On Mrr and Phase-change Materials)
2023 DEC 18 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Fresh data on Nanotechnology - Phase Change Materials are
Article Description
Traditional computers are limited by the separation of memory and processor units, is difficult to achieve fast, efficient, and low-power computing. While photonic spiking neural networks (SNNs) can overcome these shortcomings, they encounter limitations in large-scale integration. Silicon photonics platform, compatible with mature Complementary Metal Oxide Semiconductor (CMOS) platforms, is a promising candidate for realizing large-scale photonic SNNs. In this work, we proposed an integrated photonic SNN by exploiting the photonic properties of phase-change material (PCM) Ge 2 Sb 2 Te 5 (GST) and micro-ring resonators (MRR), and demonstrated its integrate-and-fire (IF) behavior. Based on a system-level behavioral model, we adopt an improved Tempotron-like ReSuMe supervised learning algorithm to train the proposed photonic SNNs and complete a pattern recognition task for the clock's 12 clockwise directions. Then the influence of different noise levels is considered, and the accuracy is close to 1 when the noise level is less than 0.2. We propose a photonic implementation of such an SNN system, and use wavelength division multiplexing to achieve a scalable architecture for the pattern recognition task. The collaborative design and optimization of hardware architecture and algorithm are realized, providing a theoretical basis for the realization of photonic SNN based on MRRs and PCM.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0030401823006181; http://dx.doi.org/10.1016/j.optcom.2023.129870; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85170210385&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0030401823006181; https://dx.doi.org/10.1016/j.optcom.2023.129870
Elsevier BV
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