Chair of Communications

Photonic Reservoir Computing


Neuromorphic signal processing (NSP) has been emerging in recent years as an alternative to classical signal processing algorithms and processes. Among others, optical communication systems can benefit from NSP due to its ability to compensate nonlinearity impairments. Instead of programming the processing tasks explicitly into a digital signal processor (DSP) or field-programmable gate array (FPGA) NSP takes a fundamentally different approach to signal processing. It uses artificial neural networks (ANN), where the machine is trained to learn the basic physical model behind the processing task and to act accordingly. However, it is very challenging to implement such ML techniques for real-time signal processing at the required line rates of up to several hundred Gb/s. This will become even more difficult in the future when signal line rates (and associated bandwidths) further scale exponentially. It can be currently foreseen that the signal bandwidth of electronic circuits will be limited in the range of 100 GHz to a maximum of a few hundred GHz in the medium term. Thus, it is desirable to shift some signal processing tasks to the optical domain, where a much higher bandwidth of multiple THz is available already today.

Photonic reservoir computing (RC) has the ability to be implemented as scalable hardware, which is unique among other ANNs. In RCs, only the input and output of the (artificial neural) network need to be adaptive and not the network itself. In fact, the interconnections are considered to be a ‘black box’, which allows to freely define the neural nodes (perceptrons). Since the nonlinear transformation to a higher dimensional state is done inside the reservoir, the output becomes a linear problem.

The primary objective of our research project is to analyze a photonic reservoir computer based on a silicon micro-ring structure to compensate for the impairments of a fiber-optic transmission system. Such ring structures are particularly well suited for integration in existing microelectronics foundries.



in cooperation with         tu-dresden





  1. S. Li, S. Pachnicke, "(Invited) Photonic Reservoir Computing in Optical Transmission Systems", IEEE Summer Topicals, Cabo San Lucas, Mexico, July 2020.
  2. S. Li, S. Dev, K. Jamshidi, S. Pachnicke, "Photonic Reservoir Computing enabled by Active Silicon Micro-Rings with Transparent Signal Injection", Conference on Lasers and Electro-Optics (CLEO 2020), San Jose, USA, May 2020.
  3. S. Dev, S. Li, S. Pachnicke, K. Jamshidi, "Optimization of a silicon micro-ring resonator for reservoir computing", ITG-Workshop "Modelling of Photonic Components & Systems", Karlsruhe, Germany, February 2020.
  4. S. Li, S. Dev, S. Ohlendorf, K. Jamshidi, S. Pachnicke, "Photonic Reservoir Computing enabled by Silicon Micro-Rings", Asia Communications and Photonics Conference (ACP 2019), Chengdu, China, November 2019.
  5. S. Li, S. Ohlendorf, S. Pachnicke, "100 km 56 GBd PAM-4 Transmission using Photonic Reservoir Computing", European Conference on Optical Communication (ECOC 2019), Dublin, Ireland, September 2019.
  6. S. Li, S. Pachnicke, "Evaluation of Photonic Reservoir Computing for Use in Short Reach PAM-4 Transmission Systems", ITG Conference "Photonic Networks", Leipzig, Germany, May 2019.