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Shadad Watad

Publications and source records attributed to Shadad Watad.

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Optoelectronic Reservoir Computing with an On-Chip True-Time-Delay Element

Compact delay elements remain a central challenge in photonic neuromorphic processors. Here, we demonstrate an optoelectronic delayed-feedback reservoir computer that incorporates a foundry-fabricated silicon nitride (Si$_3$N$_4$) true-time-delay circuit within its feedback loop. Eight cascaded Archimedean spirals provide a \SI{1.76}{\metre} on-chip optical path and a calculated passive group delay of \SI{11.63}{\nano\second}. At a feedback gain of $G=0.0223$, the system classifies sinusoidal and square waveforms without error (word error rate, WER\,$=$\,0 across all cross-validation folds), predicts the Mackey--Glass chaotic series with a best-fold normalized mean-square error (NMSE) of $0.0082$, and performs nine-class Japanese Vowels speaker classification with WER\,$=$\,0.0898. We also introduce a subcarrier phase-encoding method that maps the calculated $0.232\pi$ spiral-to-reference phase contrast onto the measured reservoir states through deliberate aliasing, achieving NMSE\,$=$\,0.0767 and WER\,$=$\,0 without increasing the insertion-loss-limited feedback gain. These results show that on-chip propagation delay in Si$_3$N$_4$ can operate as a functional component of an optoelectronic reservoir computer and motivate lower-loss, more-integrated implementations.

physics.optics

In Situ Optimization of an Optoelectronic Reservoir Computer with Digital Delayed Feedback

Reservoir computing (RC) is an innovative paradigm in neuromorphic computing that leverages fixed, randomized, internal connections to address the challenge of overfitting. RC has shown remarkable effectiveness in signal processing and pattern recognition tasks, making it well-suited for hardware implementations across various physical substrates, which promise enhanced computation speeds and reduced energy consumption. However, achieving optimal performance in RC systems requires effective parameter optimization. Traditionally, this optimization has relied on software modeling, limiting the practicality of physical computing approaches. Here, we report an \emph{in situ} optimization method for an optoelectronic delay-based RC system with digital delayed feedback. By simultaneously optimizing five parameters, normalized mean squared error (NMSE) of 0.028, 0.561, and 0.271 is achieved in three benchmark tasks: waveform classification, time series prediction, and speech recognition outperforming simulation-based optimization (NMSE 0.054, 0.543, and 0.329, respectively) in the two of the three tasks. This method marks a significant advancement in physical computing, facilitating the optimization of RC and neuromorphic systems without the need for simulation, thus enhancing their practical applicability.

cs.ET