| Authors: | C. Ma, S. Sackesyn, J.Dambre, P. Bienstman | | Title: | All-Optical Readout for Integrated Photonic Reservoir Computing | | Format: | International Conference Presentation | | Publication date: | 7/2019 | | Journal/Conference/Book: | 2019 21st International Conference on Transparent Optical Networks (ICTON)
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| | Editor/Publisher: | IEEE, | | Volume(Issue): | p.paper Fr.D5.4 (4 pages) | | Location: | Angers, France | | DOI: | 10.1109/ICTON.2019.8840492 | | Citations: | 1 (Dimensions.ai - last update: 17/11/2024)
1 (OpenCitations - last update: 27/6/2024)
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Abstract
Photonic neuromorphic computing has gained a lot of attention for its strong potential to deliver machine learning computation capability at high bitrates (> 32 Gbps) with very low energy consumption. Reservoir computing is one of the strong candidates that delivers a huge advantage on a real hardware implementation. However, one of the challenges is that current readout systems are the bottleneck of the high-speed link involving heavy power consumption from opto-electrical conversions. In this paper, we present our design of an integrated all-optical readout system that overcomes the challenges with optical weighting elements, which works at 32 Gbps and can deliver computation capability on-chip with one final readout signal channel. We especially compare the memory capability difference between the optical readout scheme with conventional electrical readout and show that optical readout is superior. Furthermore, this paper discusses the problem of some non-volatile optical weighting elements having limited weighting resolution. As a result, our readout system can still perform very well at an low resolution (4 bit) compared to using full resolution weighting elements. Related Research Topics
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