Optically isotropic longitudinal piezoelectric resonant photoelastic modulator for wide angle polarization modulation at megahertz frequencies

Polarization modulators have a broad range of applications in optics. The acceptance angle of a free-space polarization modulator is crucial for many applications. Polarization modulators that can achieve a wide acceptance angle are constructed by attaching a piezoelectric transducer to an isotropic material, and utilize a resonant transverse interaction between light and acoustic waves. Since their demonstration in the 1960s, the design of these modulators has essentially remained the same with minor improvements in the following decades. In this work, we show that a suitable single crystal with the correct crystal orientation, functioning as both the piezoelectric transducer and the acousto-optic interaction medium, could be used for constructing a highly efficient free-space resonant polarization modulator operating at megahertz frequencies and exhibiting a wide acceptance angle. We construct the modulator using gallium arsenide, an optically isotropic and piezoelectric crystal, and demonstrate polarization modulation at 6 MHz with an input aperture of 1 cm in diameter, acceptance angle reaching $\pm30^\circ$, and modulation efficiency exceeding 50%. Compared to state-of-the-art resonant photoelastic modulators, the modulator reported in this work exhibits greater than 50 fold improvement in modulation frequency for the same input aperture, while simultaneously reducing the thickness by approximately a factor of 80. Increasing the modulation frequency of photoelastic modulators from the kilohertz to the megahertz regime and substantially reducing their thickness lead to significant performance improvements for various use cases. This technological advancement also creates opportunities for utilizing these devices in new applications.Comment: 19 pages, 10 figure

Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systems

As Moore's Law loses momentum, improving size, performance, and efficiency of processors has become increasingly challenging, ending the era of predictable improvements in hardware performance. Meanwhile, the widespread incorporation of high-definition sensors in consumer devices and autonomous technologies has fueled a significant upsurge in sensory data. Current global trends reveal that the volume of generated data already exceeds human consumption capacity, making AI algorithms the primary consumers of data worldwide. To address this, a novel approach to designing AI-centric sensing systems is needed that can bridge the gap between the increasing capabilities of high-definition sensors and the limitations of AI processors. This paper provides an overview of efficient sensing and perception methods in both AI and sensing domains, emphasizing the necessity of co-designing AI algorithms and sensing systems for dynamic perception. The proposed approach involves a framework for designing and analyzing dynamic AI-in-the-loop sensing systems, suggesting a fundamentally new method for designing adaptive sensing systems through inference-time AI-to-sensor feedback and end-to-end efficiency and performance optimization

Polarization-insensitive wide-angle resonant acousto-optic phase modulator

Phase modulators are commonly used devices in optics. Free-space phase modulators are typically constructed from optically anisotropic crystals exhibiting the Pockels effect. To preserve the light's polarization state as it propagates through the crystal, it is essential to align the polarization and angle of incidence of the light with respect to the crystal. In this study, we demonstrate the feasibility of constructing free-space resonant phase modulators with a broad acceptance angle and minimal dependence on the polarization state of light using an acousto-optic approach. These modulators operate in the megahertz frequency range, require modest power levels, have aperture sizes exceeding one square centimeter, and feature sub-millimeter thickness.Comment: 8 pages, 6 figure

Adaptive Inference: Theoretical Limits and Unexplored Opportunities

This paper introduces the first theoretical framework for quantifying the efficiency and performance gain opportunity size of adaptive inference algorithms. We provide new approximate and exact bounds for the achievable efficiency and performance gains, supported by empirical evidence demonstrating the potential for 10-100x efficiency improvements in both Computer Vision and Natural Language Processing tasks without incurring any performance penalties. Additionally, we offer insights on improving achievable efficiency gains through the optimal selection and design of adaptive inference state spaces