8 papers
DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
Xinling Yu, Yixing Li, Ziyue Liu +4
Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve. Operator-learning surrogates replace this solve with a fas…
Scalable Digital Compute-in-Memory Ising Machines for Robustness Verification of Binary Neural Networks
Madhav Vadlamani, Rahul Singh, Yuyao Kong +2
Verification of binary neural network (BNN) robustness is NP-hard, as it can be formulated as a combinatorial search for an adversarial perturbation that induces misclassification.…
Tensor-Compressed and Fully-Quantized Training of Neural PDE Solvers
Jinming Lu, Jiayi Tian, Yequan Zhao +2
Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into neural network train…
Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators
Jinsong Zhang, Minghe Li, Jiayi Tian +2
High-order tensor decomposition has been widely adopted to obtain compact deep neural networks for edge deployment. However, existing studies focus primarily on its algorithmic adv…
FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training
Jinming Lu, Jiayi Tian, Hai Li +2
The increasing demand for on-device training of deep neural networks (DNNs) aims to leverage personal data for high-performance applications while addressing privacy concerns and r…
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
Xinling Yu, Ziyue Liu, Hai Li +5
Thermal analysis is crucial in 3D-IC design due to increased power density and complex heat dissipation paths. Although operator learning frameworks such as DeepOHeat~\cite{liu2023…