Hotspot detection is a crucial step in ensuring the manufacturability of integrated circuits, as it seeks to identify potential defects in the layout. Pattern matching methods have been widely used to accelerate the detection of these defects. However, they often struggle with complicated deviations. Image-based machine learning methods were introduced to confront this challenge, but they often involved distorted information extraction and incurred significant runtime overhead. In this article, we introduce a novel detection framework based on the modified transitive closure graph (MTCG). By applying the concept of MTCG, the layout can be accurately modeled as a heterograph. The embeddings of these heterographs are extracted using an optimized lightweight 3-hop message-passing graph neural network (GNN) and subsequently utilized for classification. Furthermore, a dynamic edge transformation method based on the properties of MTCG is proposed for data augmentation. The proposed method is evaluated with datasets from ICCAD 2012 and ICCAD 2019, demonstrating outstanding performance in recall and false alarm, along with a significantly decreased inference time.

Advanced technology nodes face challenges with Design Rule Violations (DRVs), primarily due to the possibility of nm-level small variations that can lead to the occurrence of DRVs. Various Machine Learning (ML) techniques have been introduced to detect whether the layout design conforms to manufacturing rules, thus alleviating the time-consuming challenge associated with traditional lithography simulations. However, existing ML models still face challenges in detecting layout violations where there are pose variations among geometric shapes in the layout. In this study, we propose a hotspot detector called PSCaps based on the CapsNet, which considers the pose information of geometric shapes in the layout. The method effectively captures spatial information and hierarchical structures between geometric shapes in the layout. Through the dynamic routing mechanism, the model adaptively learns the relationships and weight allocations between different capsules. Additionally, we employ multiple data augmentation methods to alleviate the problem of imbalanced hotspot and non-hotspot data in the open-source dataset. The benchmarks of ICCAD-2012 and ICCAD-2019 are used to validate our method. The experimental results demonstrate that our proposed hotspot detector outperforms other state-of-the-art works.

The rapid growth of rich multimedia data in today’s Internet, especially video traffic, has challenged the content delivery networks (CDNs). Caching serves as an important means to reduce user access latency so as to enable faster content downloads. Motivated by the dynamic nature of the real-world edge traces, this paper introduces aprovably wellonline caching policy in dynamic environments where: 1) the popularity is highly dynamic; 2) no regular stochastic pattern can model this dynamic evaluation process. First, we design an online optimization framework, which aims to minimize thedynamic regretthat finds the distance between an online caching policy and the best dynamic policy in hindsight. Second, we propose a dynamic online learning method to solve the non-stationary caching problem formulated in the previous framework. Compared to the linear dynamic regret of previous methods, our proposal is proved to achieve asublinear dynamic regret, from which it is guaranteed to be nearly optimal. We verify the design using both synthetic and real-world traces: the proposed policy achieves the best performance in the synthetic traces with different levels of dynamicity, which verifies the dynamic adaptation; our proposal consistently achieves at least 9.4% improvement than the baselines, including LRU, LFU, Static Online Learning based replacement, and Deep Reinforcement Learning based replacement, in random edge areas from real-world traces (from iQIYI), further verifying the effectiveness and robustness on the edge.

Short videos have received extraordinary success in recent years. To provide smooth playback and avoid rebuffering delay, prefetching upcoming videos is commonly used in cellular networks. Current prefetching designs fall short in dealing with bandwidth overhead, especially the exit overhead of downloaded but unconsumed chunks due to user exit. Measurement from a large short video platform shows that exit overhead accounts for up to 43.5% of bandwidth overhead. Thus we build Alfie, a bandwidth-efficient short video prefetching algorithm via reinforcement learning. Essentially Alfie adjusts prefetching based upon user viewing patterns in addition to network conditions. We demonstrate that Alfie outperforms the state of the art by up to 26.8% in overall performance while reducing the exit overhead by up to 84.9%.

The infrastructure for multimedia content delivery has been using more and more edge infrastructure (e.g., base stations, smart routers, etc.), which not only alleviates the centralized servers but also improves the quality of service by letting users access content nearby. Algorithms based on deep reinforcement learning (DRL) have been widely adopted by such edge cache replacement strategies due to their capability to adapt to changing request patterns. However, a DRL cache replacement agent learns extremely slow at an edge cache because of the sparse requests. In this paper, we propose a popularity distillation framework that allows edge caches to refer to content replication strategies of other edge caches. First, we design a collaborative edge cache framework that lets edge caches learn their strategies by handling the local requests using deep reinforcement learning and learn from others by exchanging the "soft" popularity distributions experienced by different edge caches. Second, we design a neighbor maintenance mechanism in which an agent iteratively selects only a small number of neighboring edge caches to perform the collaboration. Experiments driven by a real-world mobile video dataset show that our design can improve the cache hit rate by 3.0% compared with a non-popularity distillation baseline with only a small overhead of transmission data during distillation.