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...

Among their benefits is the potential for parallel processing using mature technologies such as wavelength division multiplexing (WDM) which has been applied to photonic neural networks [2]. In our previous study [3], we showed the potential of WDM to improve the computing capabilities of a reservoir computing (RC) scheme. RC is a relatively recent computing paradigm that uses random fixed weights and complex nonlinear dynamics to map the input data to a higher dimensional space. This process allows simplifying its training process, which is only required in its output layer (usually through linear or ridge regression). The performance of RC is highly related to the nonlinear dynamics of the nodes and the capability to buffer past inputs (memory) [1]. Nevertheless, photonic RC schemes may reduce their scalability if multiple photonic blocks are implemented as nonlinear nodes.