Multi-task Photonic Reservoir Computing: Wavelength Division Multiplexing for Parallel Computing with a Silicon Microring Resonator

TL;DR

This work tackles the bottleneck in conventional computing by deploying a multitask photonic reservoir computer based on time-delay RC in a silicon microring resonator, extended with wavelength-division multiplexing to run multiple tasks in parallel. Each channel is detuned from a different MRR resonance, forming independent computing roads that share nonlinear dynamics, modeled via temporal coupled-mode theory. The authors demonstrate four benchmark tasks (NARMA-10, SWC, ChEq, IPIX radar) and analyze memory, nonlinearity, and phase-control effects, showing that parallel operation can approach single-task performance while offering substantial throughput gains. Phase tuning in the external delay line emerges as a practical knob to optimize multitask performance, enabling scalable parallel photonic processing with maintained memory and nonlinear capacity across channels.

Abstract

Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solution to the infamous bottleneck between memory and computing processors, which reduces the effective throughput of data. In photonics, novel schemes attempt to collocate the computing processor and memory in a single device. Photonics offers the flexibility of multiplexing streams of data not only spatially and in time, but also in frequency or, equivalently, in wavelength, which makes it highly suitable for parallel computing. Here, we numerically show the use of time and wavelength division multiplexing (WDM) to solve four independent tasks at the same time in a single photonic chip, serving as a proof of concept for our proposal. The system is a time-delay reservoir computing (TDRC) based on a microring resonator (MRR). The addressed tasks cover different applications: Time-series prediction, waveform signal classification, wireless channel equalization, and radar signal prediction. The system is also tested for simultaneous computing of up to 10 instances of the same task, exhibiting excellent performance. The footprint of the system is reduced by using time-division multiplexing of the nodes that act as the neurons of the studied neural network scheme. WDM is used for the parallelization of wavelength channels, each addressing a single task. By adjusting the input power and frequency of each optical channel, we can achieve levels of performance for each of the tasks that are comparable to those quoted in state-of-the-art reports focusing on single-task operation...

Figures (11)

• Figure 1: WDM MRR-based TDRC scheme addressing four different tasks (top right inset): Channel 0: NARMA-10 time-series prediction. Channel 1: Signal waveform classification (SWC). Channel 2: Wireless channel equalization (ChEq). Channel 3: Radar signal prediction. On the bottom left, is the frequency allocation of the optical channels. PD: Photodiode. RR: Ridge regression.
• Figure 2: Regions A, B, and C in terms of $\overline{P}_{\textrm{in}}$ and $\Delta\omega/2\pi$, when solving the NARMA-10 task.
• Figure 3: NMSE of the 4-channels WDM MRR-based TDRC addressing equal instances of the same task (NARMA-10) simultaneously as a function of $\overline{P}_{\textrm{i}}$ and $\Delta\omega_i/2\pi$. a) Channel 0, b) Channel 1, c) Channel 2, d) Channel 3. The best performance is encircled in red.
• Figure 4: NMSE of the 4-channels WDM MRR-based TDRC addressing different instances of the same task (NARMA-10) simultaneously as a function of $\overline{P}_{\textrm{i}}$ and $\Delta\omega_i/2\pi$. a) Channel 0, b) Channel 1, c) Channel 2, d) Channel 3. The best performance is encircled in red.
• Figure 5: NARMA-10 NMSE per $\omega_i$ as a function of $\Delta\omega_i/2\pi$ for $\overline{P}_{\textrm{T}} = 0$ dBm, when varying the number of WDM channels: a) $M$ = 1, b) $M$ = 2, c) $M$ = 3, d) $M$ = 4, e) $M$ = 5, f) $M$ = 10. SP: Self-pulsing.