Co-packaged optics (CPO): status, challenges, and solutions.

Due to the rise of 5G, IoT, AI, and high-performance computing applications, datacenter traffic has grown at a compound annual growth rate of nearly 30%. Furthermore, nearly three-fourths of the datacenter traffic resides within datacenters. The conventional pluggable optics increases at a much slower rate than that of datacenter traffic. The gap between application requirements and the capability of conventional pluggable optics keeps increasing, a trend that is unsustainable. Co-packaged optics (CPO) is a disruptive approach to increasing the interconnecting bandwidth density and energy efficiency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and photonics. CPO is widely regarded as a promising solution for future datacenter interconnections, and silicon platform is the most promising platform for large-scale integration. Leading international companies (e.g., Intel, Broadcom and IBM) have heavily investigated in CPO technology, an inter-disciplinary research field that involves photonic devices, integrated circuits design, packaging, photonic device modeling, electronic-photonic co-simulation, applications, and standardization. This review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform, identify the key challenges, and point out the potential solutions, hoping to encourage collaboration between different research fields to accelerate the development of CPO technology.

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds.

Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey, which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems-ommatidia, motion pathway, contrast pathway, and mushroom body. Compared with the existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated into the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over the existing STMD-based models against fake features.