Thermal analysis is increasingly critical in modern integrated circuits, where non-uniform power dissipation and high transistor densities can cause rapid temperature spikes and reliability concerns. Traditional methods, such as FEM-based simulations, offer high accuracy but are computationally prohibitive for early-stage design, often requiring multiple iterative redesign cycles to resolve late-stage thermal failures. To address these challenges, we propose ‘2D-ThermAl’, a physics-informed generative AI framework that effectively identifies heat sources and estimates full-chip transient and steady-state thermal distributions directly from input activity profiles. ThermAl employs a hybrid U-Net architecture enhanced with positional encoding and a Boltzmann regularizer to maintain physical fidelity. Our model is trained on an extensive dataset of heat dissipation maps for over 200 circuit configurations, ranging from simple logic gates (e.g., inverters, NAND, XOR) to complex designs, generated using COMSOL and Cadence EDA flows. The dataset captures diverse activity patterns, and we note that material-dependent thermal properties may require targeted fine-tuning to ensure accuracy across different fabrication contexts. Experimental results demonstrate that 2D-ThermAl provides precise temperature mappings for large circuits, with a root mean squared error (RMSE) of only 0.71°C and outperforms conventional FEM tools by running up to ∼ 200× faster. We analyze performance across diverse layouts and workloads and discuss its applicability to large-scale EDA workflows. Although thermal reliability assessments often extend beyond 85°C for post-layout signoff, our focus here is on early-stage hotspot detection and thermal pattern learning. To ensure generalization beyond the nominal operating range (25−55°C), we additionally performed cross-validation on an extended dataset that spans 25−95°C, maintaining a high accuracy (< 2.2% full-scale RMSE) even under elevated temperature conditions representative of the peak power and stress scenarios. Limitations such as 2D-only modeling and real-world validation are addressed with concrete future directions, including 3D extension, generalization across technology nodes, and transfer learning strategies. The code and dataset are publicly available at: https://github.com/soumyadeepchandra/2D-ThermAl.

Procedure learning refers to the task of identifying the key-steps and determining their logical order, given several videos of the same task. For both third-person and first-person (egocentric) videos, state-of-the-art (SOTA) methods aim at finding correspondences across videos in time to accomplish procedure learning. However, to establish temporal relationships within the sequences, these methods often rely on frame-to-frame mapping, or assume monotonic alignment of video pairs, leading to sub-optimal results. To this end, we propose to treat the video frames as samples from an unknown distribution, enabling us to frame their distance calculation as an optimal transport (OT) problem. Notably, the OT-based formulation allows us to relax the previously mentioned assumptions. To further improve performance, we enhance the OT formulation by introducing two regularization terms. The first, inverse difference moment regularization, promotes transportation between instances that are homogeneous in the embedding space as well as being temporally closer. The second, regularization based on the KL-divergence with an exponentially decaying prior smooths the alignment while enforcing conformity to the optimality (alignment obtained from vanilla OT optimization) and temporal priors. The resultant optimal transport guided procedure learning framework (`OPEL') significantly outperforms the SOTA on benchmark datasets. Specifically, we achieve 22.4\% (IoU) and 26.9\% (F1) average improvement compared to the current SOTA on large scale egocentric benchmark, EgoProceL. Furthermore, for the third person benchmarks (ProCeL and CrossTask), the proposed approach obtains 46.2\% (F1) average enhancement over SOTA.