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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. Graphical Abstract

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Graph-level representation learning is the pivotal step for downstream tasks that operate on the whole graph. The most common approach to this problem heretofore is graph pooling, where node features are typically averaged or summed to obtain the graph representations. However, pooling operations like averaging or summing inevitably cause massive information missing, which may severely downgrade the final performance. In this paper, we argue what is crucial to graphlevel downstream tasks includes not only the topological structure but also the distribution from which nodes are sampled. Therefore, powered by existing Graph Neural Networks (GNN), we propose a new plug-and-play pooling module, termed as Distribution Knowledge Embedding (DKEPool), where graphs are rephrased as distributions on top of GNNs and the pooling goal is to summarize the entire distribution information instead of retaining a certain feature vector by simple predefined pooling operations. A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning. Structure learning follows a recursive neighborhood aggregation scheme to update node features where structure information is obtained. Distribution learning, on the other hand, omits node interconnections and focuses more on the distribution depicted by all the nodes. Extensive experiments demonstrate that the proposed DKEPool significantly and consistently outperforms the state-of-the-art methods.