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Queen’s University in Canada is a leading research institution with strengths in AI, computing, and healthcare innovation. It fosters interdisciplinary discovery and leadership in public service. NEC Labs America collaborates with Queen’s University on cross-lingual modeling, privacy-preserving translation, and low-latency speech processing. Please read about our latest news and collaborative publications with Queen’s University.

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Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models

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When performing complex multi-step reasoning tasks, the ability of Large Language Models (LLMs) to derive structured intermediate proof steps is important for ensuring that the models truly perform the desired reasoning and for improving models’ explainability. This paper is centered around a focused study: whether the current state-of-the-art generalist LLMs can leverage the structures in a few examples to better construct the proof structures with in-context learning. Our study specifically focuses on structure-aware demonstration and structure-aware pruning. We demonstrate that they both help improve performance. A detailed analysis is provided to help understand the results.

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Low-Latency Passive Thermal Stabilization of a Silicon Micro-Ring Resonator with Self-Heating

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Analog photonic information processing can be implemented with low chip area using wavelength-division multiplexed systems, which typically manipulate light using micro-ring resonators. Micro-rings are uniquely susceptible to thermal crosstalk, with negative system performance consequences if not addressed. Existing thermal sensitivity mitigation methods face drawbacks including high complexity, high latency, high digital and analog hardware requirements, and CMOS incompatibility. Here, we demonstrate a passive thermal desensitization mechanism for silicon micro-ring resonators exploiting self-heating resulting from optical absorption. We achieve a 49% reduction in thermal crosstalk sensitivity and 1 ?s adaptation latency using a system with no specialized micro-ring engineering, no additional control hardware, and no additional calibration. Our theoretical model indicates the potential for significant further desensitization gains with optimized microring designs. Self-heating desensitization can be combined with active thermal stabilization to achieve both responsiveness and accuracy or applied independently to thermally desensitize large photonic systems for signal processing or neural network inference.

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Multi-terminal Germanium Photodetector in a Commercial Silicon Photonics Platform

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We report responsivity measurements of a multiterminal photodetection device in a commercial silicon photonics platform. The ratio of measured responsivities is found to track the relative terminal lengths. This can serve as a highly compact optoelectronic tap/diplexer. More importantly, complex biasing conditions of similar devices are promising for onchip reprogrammable opto-electronic responses in conventional silicon photonic platforms, with applications in reprogrammable photonics and neuromorphic photonics.

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Link Loss Analysis of Integrated Linear Weight Bank within Silicon Photonic Neural Network

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Over the last decade, silicon photonic neural networks have demonstrated the possibility of photonic-enabled machine learning at the edge. These systems enable low-latency ultra-wideband classifications, channel estimations, and many other signal characterization tasks within wireless environments. While these proof-of-concept experiments have yielded promising results, poor device and architectural designs have resulted in sub-optimal bandwidth and noise performance. As a result, the application space of this technology has been limited to GHz bandwidths and high signal-to-ratio input signals. By applying a microwave photonic perspective to these systems, the authors demonstrate high-bandwidth operation while optimizing for RF performance metrics: instantaneous bandwidth, link loss, noise figure, and dynamic range. The authors explore the extended capabilities due to these improved metrics and potential architectures to continue further optimization. The authors introduce novel architectures and RF analysis for RF-optimized neuromorphic photonic hardware.

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