2.5D chiplet-based technology promises an efficient integration technique for advanced designs with more functionality and higher performance. Temperature and related thermal optimization, heat removal are of critical importance for temperature-aware physical synthesis for chiplets. This paper presents a novel graph convolutional networks (GCN) architecture to estimate the thermal map of the 2.5D chiplet-based systems with the thermal resistance networks built by the compact thermal model (CTM). First, we take the total power of all chiplets as an input feature, which is a global feature. This additional global information can overcome the limitation that the GCN can only extract local information via neighborhood aggregation. Second, inspired by convolutional neural networks (CNN), we add skip connection into the GCN to pass the global feature directly across the hidden layers with the concatenation operation. Third, to consider the edge embedding feature, we propose an edge-based attention mechanism based on the graph attention networks (GAT). Last, with the multiple aggregators and scalers of principle neighborhood aggregation (PNA) networks, we can further improve the modeling capacity of the novel GCN. The experimental results show that the proposed GCN model can achieve an average RMSE of 0.31 K and deliver a 2.6$\times$ speedup over the fast steady-state solver of open-source {\it HotSpot} based on SuperLU. More importantly, the GCN model demonstrates more useful generalization or transferable capability. Our results show that the trained GCN can be directly applied to predict thermal maps of six unseen datasets with acceptable mean RMSEs of less than 0.67 K without retraining via inductive learning.
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This content will become publicly available on December 1, 2024
Inductive detection of influence operations via graph learning
Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations that evade detection and influence public discourse on social media with greater scale, reach, and specificity. New methods of detection with inductive learning capacity will be needed to identify novel operations before they indelibly alter public opinion and events. To this end, we develop an inductive learning framework that: (1) determines content- and graph-based indicators that are not specific to any operation; (2) uses graph learning to encode abstract signatures
of coordinated manipulation; and (3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators-illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.
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- Award ID(s):
- 2125677
- NSF-PAR ID:
- 10521622
- Publisher / Repository:
- Nature Portfolio
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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