The rapid advancement of deep learning, with increasingly large deep neural networks (DNNs), has led to the use of multi-core parallel processing in accelerators, utilizing Network-on-Chip (NoC) for interconnection. However, while multi-core architectures improve computational performance, they also increase data movement overhead. This paper analyzes NoC traffic patterns during DNN processing and explores mapping optimization to reduce computational and communicational overheads. We propose NoC-based Accelerators Mapping Exploration (NAME), an automated mapper for generating high-performance task mappings for NoC-based accelerators. NAME balances computational and communication overheads by dividing DNNs into scalable groups and interleaving NoC link usage, reducing traffic bottlenecks. Experiments show that NAME reduces execution time by up to 82% and 52%, respectively, compared to fixed mapping and the state-of-the-art framework AOME (Autonomous Optimal Mapping Exploration).

The rise of social platforms based on online social networks has greatly enriched people’s lives, resulting in various applications. Traditional research mainly focuses on users but pays less attention to the edges between users, and they all assume the topology of social networks is known in advance. Indeed, obtaining the network topology is challenging because of privacy protection and business competition. In this paper, we propose an activity initiation problem inspired by real business applications, such as Pinduoduo and Tencent, where each edge can be abstracted as an activity in which both ends (users) of the edge participate together, and the edge can be initiated by one of them. At this time, we hope to select as few users as possible to initiate activities and make the users of the whole network participate together. This problem can be reduced to the classic vertex cover problem, but the network information is hidden by social platforms as much as possible. To address this challenge, we put forward a solver-detector model. In each round of interaction, the solver uses a detector to obtain a small amount of edge information and achieves vertex coverage. This is a model in which a solver has limited access to input, but still gives a 2-approximation that is as good as the conventional model. Finally, we can cover the whole network with very few edge samples, which is a brand-new research perspective.