Publications

Publications that acknowledge TILOS (NSF CCF-2112665) support

C.-K. Cheng, A. B. Kahng, S. Kundu, Y. Wang and Z. Wang, "Assessment of Reinforcement Learning for Macro Placement", (.pdf), Proc. ACM/IEEE Intl. Symp. on Physical Design, 2023, to appear. (Invited Paper) [arXiv]
E. Sebastián, T. Duong, N. Atanasov, E. Montijano and C. Sagues, “LEMURS: Learning Distributed Multi-Robot Interactions”, IEEE Intl. Conf. on Robotics and Automation (ICRA), 2023.
X. Cheng, J. Zhang, and S. Sra, “Theory and Algorithms for Diffusion Processes on Riemannian Manifolds”, arXiv preprint arXiv:2204.13665. (Link)
J. Cervino, L. Ruiz and A. Ribeiro, “Learning by Transference: Training Graph Neural Networks on Growing Graphs”, IEEE Transactions on Signal Processing, 2023.
J. Cervino, L. Chamon, B. D Haeffele, R. Vidal and A. Ribeiro, “Learning Globally Smooth Functions on Manifolds”, in submission.
X. Liu, A. Prabhu, F. Cladera, I. D. Miller, L. Zhou, C. J. Taylor and V. Kumar, “Active Metric-Semantic Mapping by Multiple Aerial Robots,” Intl. Conf. on Robotics and Automation, 2023, to appear.
Y. Tao, Y. Wu, F. Cladera and V. Kumar, “SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain,” Intl. Conf. on Robotics and Automation, 2023, to appear.
K. Bhatia, Y.-A. Ma, A. D. Dragan, P. L. Bartlett and M. I. Jordan, "Bayesian robustness: A nonasymptotic viewpoint", Journal of the American Statistical Association, (2023).
B. Li, Y.-A. Ma, J. N. Kutz and X. Yang, "The Adaptive Spectral Koopman Method for Dynamical Systems", SIAM Journal on Applied Dynamical Systems, (to appear).
A. Guelen, F. Memoli, Z. Wan and Y. Wang, "A generalization of the persistent Laplacian to simplicial maps", Intl. Symp. Comput. Geom. (SoCG), to appear, 2023.
D. Lim, J. D. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron and S. Jegelka, "Sign and Basis Invariant Networks for Spectral Graph Representation Learning" Intl. Conf. on Learning Representations (ICLR), 2023. (Spotlight/notable-top-25%)
X. Xia, G. Mishne, and Y. Wang, "Implicit Graphon Neural Representation", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), to appear, 2023. (Oral Presentation) (Top 1.9% of submissions)
B. Tahmasebi, D. Lim and S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures", Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2023. (Oral Presentation) (Top 1.9% of submissions)
X. Yu, L. Cherkasova, H. Vardhan, Q. Zhao, E. Ekaireb, X. Zhang, A. Mazumdar and T. Rosing, "Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks", ACM/IEEE Conf. on Internet of Things Design and Implementation (IoTDI), 2023.
A. F. Budak, D. Smart, B. Swahn and D. Z. Pan, “APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors using DNN Learning,” Proc. IEEE/ACM Asia and South Pacific Design Automation Conference (ASPDAC), January 2023.
S. Kumar, J. Zamora, N. Hansen, R. Jangir and X. Wang, “Graph Inverse Reinforcement Learning from Diverse Videos”, Conf. on Robot Learning (CoRL), 2022 (Oral Presentation).
Y. Wu, J. Wang and X. Wang, “Learning Generalizable Dexterous Manipulation from Human Grasp Affordance”, CoRL, 2022.
Y. Qin, B. Huang, Z. Yin, H. Su and X. Wang, “Generalizable Point Cloud Policy Learning for Sim-to-Real Dexterous Manipulation”, CoRL, 2022.
C. Yu, H. Yu and S. Gao, "Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation", CoRL (Conference on Robot Learning), 2022.
M. Weber and S. Sra, “Computing Brascamp-Lieb Constants through the lens of Thompson Geometry,” arXiv preprint arXiv:2208.05013, (link).
S. Sra and M. Weber, “On a class of geodesically convex optimization problems solved via Euclidean MM methods,” arXiv preprint arXiv:2206.11426, (link).
O. Mangoubi and N. K. Vishnoi, “Re-Analyze Gauss: Bounds for private matrix approximation via Dyson Brownian motion,” Annual Conf. on Neural Information Processing Systems (NeurIPS), 2022. (Link).
A. Mehrotra and N. K. Vishnoi, “Fair ranking with noisy protected attributes,” NeurIPS, 2022. (Link)
O. Mangoubi and N. K. Vishnoi, “Sampling from log-concave distributions with infinity-distance guarantees,” NeurIPS, 2022. (Link)
X. Cheng, J. Zhang, and S. Sra, “Efficient Sampling on Riemannian Manifolds via Langevin MCMC,” NeurIPS, 2022.
Y. Zhai and S. Gao, "Monte Carlo Tree Descent for Black-Box Optimization", NeurIPS, 2022.
R. Zhang, C. Yu, J. Chen, C. Fan and S. Gao, "Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding", NeurIPS, 2022.
E. Y. Yu, Z. Qin, M. K. Lee and S. Gao, "Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems", NeurIPS, 2022.
N. Chandramoorthy, A. Loukas, K. Gatmiry and S. Jegelka, “On the generalization of learning algorithms that do not converge,” NeurIPS, 2022.
N. Karalias, J. Robinson, A. Loukas and S. Jegelka, “Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions,” NeurIPS, 2022.
C.-Y. Chuang and S. Jegelka, “Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks”, NeurIPS, 2022.
S. Hanneke, A. Karbasi, S. Moran and G. Velegkas, "Universal Rates for Interactive Learning", NeurIPS, 2022.
G. Velegkas, Z. Yang and A. Karbasi, "The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches", NeurIPS, 2022.
W. Li, M. Feldman, E. Kazemi and A. Karbasi, "Submodular Maximization in Clean Linear Time", NeurIPS, 2022.
I. Mehalel, S. Hanneke, S. Moran, M. Mahmoody and A. Karbasi, "On Optimal Learning Under Targeted Data Poisoning", NeurIPS, 2022.
A. Kalavasis, G. Velegkas and A. Karbasi, "Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes", NeurIPS, 2022.
I. Han, A. Zandieh, J. Lee, R. Novak, L. Xiao and A. Karbasi, "Fast Neural Kernel Embeddings for General Activations", NeurIPS, 2022.
K. E. Nikolakakis, F. Haddadpour, D. S. Kalogerias, A. Karbasi, "Black-Box Generalization", NeurIPS, 2022.
I. Bustany, A. B. Kahng, Y. Koutis, B. Pramanik and Z. Wang, "SpecPart: A Supervised Spectral Framework for Hypergraph Partitioning Solution Improvement", (.pdf), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022.
K. Zhu, H. Chen, W. Turner, G. Kokai, P. Wei, D. Z. Pan, and H. Ren, “TAG: Learning Circuit Spatial Embedding From Layouts,” Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022.
J. Jung, A. B. Kahng, R. Varadarajan and Z. Wang, “IEEE CEDA DATC: Expanding Research Foundations for IC Physical Design and ML-Enabled EDA,” (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
A. B. Kahng, “A Mixed Open-Source and Proprietary EDA Commons for Education and Prototyping,”, (.pdf), (.pptx), (.mp4), Proc. ACM/IEEE International Conference on Computer-Aided Design, 2022. (Invited Paper).
H. Esmaeilzadeh, S. Ghodrati, A. B. Kahng, J. K. Kim, S. Kinzer, S. Kundu, R. Mahapatra, S. D. Manasi, S. S. Sapatnekar, Z. Wang and Z. Zeng, "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms", (.pdf), Proc. ACM/IEEE Workshop on Machine Learning for CAD, 2022.
V. A. Chhabria, W. Jiang, A. B. Kahng, S. S. Sapatnekar, "From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction", (.pdf), Proc. ACM/IEEE Workshop on Machine Learning for CAD, 2022.
Z. Jiang, M. Liu, Z. Guo, S. Zhang, Y. Lin, and D. Z. Pan, “A Tale of EDA’s Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation,” ACM/IEEE Workshop on Machine Learning for CAD, 2022.
D. Wu, M. Chinazzi, A. Vespignani, Y.-A. Ma and R. Yu, "Multi-fidelity Hierarchical Neural Processes", KDD, 2022.
X. Wang, A. Lalitha, T. Javidi and F. Koushanfar, "Peer-to-Peer Variational Federated Learning over Arbitrary Graphs," to appear in IEEE Journal of Selected Areas in Information Theory .
D. Lim, J. Robinson, L. Zhao, T. Smidt, S. Sra, H. Maron, S. Jegelka, "Sign and Basis Invariant Networks for Spectral Graph Representation Learning," ICML workshop on Geometric and Topological Representation Learning, 2022. (in submission)
B. Tahmasebi, D. Lim, S. Jegelka, "The Power of Recursion in Graph Neural Networks for Counting Substructures," ICML workshop on Topology, Algebra and Geometry in Data Science, 2022.
S. Chen, S. Lim, F. Memoli, Z. Wan and Y. Wang, “Weisfeiler-Lehman Meets Gromov-Wasserstein”, International Conference on Machine Learning (ICML), 2022.
S. Shekar and T. Javidi, “Instance-dependent Regret Analysis of Kernelized Bandits,” Intl. Conf. on Machine Learning (ICML), 2022.
C. Cai and Y. Wang, “Convergence of Invariant Graph Networks,” Intl. Conf. on Machine Learning (ICML), 2022.
E. Lei, H. Hassani and S. S. Diokhti, “Neural Estimation of the Rate-Distortion Function With Applications to Operational Source Coding,” Proc. of IEEE Int. Symp. on Information Theory (ISIT), 2022.
M. Lee, S. Shekar and T. Javidi, “Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS,” Proc. of IEEE Int. Symp. on Information Theory (ISIT), 2022.
J. Jin and S. Sra, “Understanding Riemannian Acceleration via a Proximal Extragradient Framework,” Proc. Conference on Learning Theory, July 2022, pp. 2924-2962.
S. Jegelka, "Theory of Graph Neural Networks: Representation and Learning," Proc. of the Intl. Congress of Mathematicians (ICM), 2022.
V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent Min-Max Optimization Algorithm," Proc. of Machine Learning Research, 2022. (Link)
A. Mehrotra, B. S. Pradelski and N. K. Vishnoi, "Selection in the Presence of Implicit Bias: The Advantage of Intersectional Constraints," ACM Conf. on Fairness, Accountability, and Transparency, June, 2022. (Link)) [Also: Computing Research Repository (CoRR), February 2022. (Link) ]
O. Mangoubi, N. K. Vishnoi, "Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk," arxiv, 2022. (Link)
L. Zhou and V. Kumar, “Robust multi-robot active target tracking against sensing and communication attacks,” American Control Conf. (ACC), 2022.
H. Chen, X. Zhang, K. Huang and F. Koushanfar, “AdaTest: Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection,” ACM Transactions on Embedded Computing Systems (TECS), 2022.
E. Ekaireb, X. Yu, K. Ergun, Q. Zhao, K. Lee, M. Huzaifa, and T. Rosing, “ns3-fl: Simulating Federated Learning with ns-3,” Workshop of NS3 (WNS3), 2022.
L. Jarin-Lipschitz, X. Liu, Y. Tao and V. Kumar, "Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests," IEEE Intl. Conf. on Robotics and Automation, 2022.
K. Sun, S. Chaves, P. Martin and V. Kumar, "RTGNN: A novel approach to model stochastic traffic dynamics," IEEE Intl. Conf. on Robotics and Automation, 2022.
H. Sanghvi and C. J. Taylor, “Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models,” IEEE Intl. Conf. on Robotics and Automation, 2022.
Z. Li, T. Duong and N. Atanasov, “Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics,” IEEE Open Journal of Control Systems (OJCSYS), 2022.
Z. Li, T. Duong and N. Atanasov, “Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics,” Learning for Dynamics and Control (L4DC), 2022. (Link)
X. Liu, G. V. Nardari, F. C. Ojeda, Y. Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. F. Romero, C. J. Taylor and V. Kumar, "Large-scale autonomous flight with real-time semantic slam under dense forest canopy," IEEE Robotics and Automation Letters 7(2) (2022), pp. 5512–5519.
S. Mayya, R. K. Ramachandran, L. Zhou, V. Senthil, D. Thakur, G. S. Sukhatme and V. Kumar, "Adaptive and risk-aware target tracking for robot teams with heterogeneous sensors," IEEE Robotics and Automation Letters 7(2) (2022), pp. 5615–5622.
D. Mox, V. Kumar and A. Ribeiro, "Learning connectivity-maximizing network configurations," IEEE Robotics and Automation Letters 7(2) (2022), pp. 5552–5559.
C. Yun, S. Rajput and S. Sra, "Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond," Intl. Conf. on Learning Representations, 2022. (Link)
K. Gatmiry, S. Jegelka, J. Kelner, "Optimization and Adaptive Generalization of Three layer Neural Networks," Intl. Conf. on Learning Representations, 2022. (Link)
R. Yang, M. Zhang, N. Hansen, H. Xu and X. Wang, "Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers," Intl. Conf. on Learning Representations, 2022. (Link)
A. B. Kahng, "Leveling Up: A Trajectory of OpenROAD, TILOS and Beyond," Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022, pp. 73-79. (Link)
A. B. Kahng, R. Varadarajan and Z. Wang, “RTL-MP: Toward Practical, Human-Quality Chip Planning and Macro Placement,” Proc. ACM/IEEE Intl. Symp. on Physical Design, 2022. (Link)
T. Duong and N. Atanasov, “Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features,” IEEE Control Systems Letters (L-CSS), 2022. (Link)
O. Mangoubi and N. K. Vishnoi, "Sampling from Log-Concave Distributions with Infinity-Distance Guarantees," arXiv preprint, 2022. (Link)
A. B. Kahng, "Machine Learning for CAD/EDA: The Road Ahead," IEEE Design and Test (Special Issue on Machine Learning for CAD/EDA), to appear.
R. Jangir, N. Hansen, S. Ghosal, M. Jain and X. Wang, "Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation," Robotics and Automation Letters (RA-L), 2022. (Link)
J. Zhang, H. Li, S. Sra and A. Jadbabaie, "Rethinking Convergence in Deep Learning: Beyond Stationary Points," arXiv:2110.06256, 2022. (Preprint)
R. Shen, L. Gao and Y. Ma, “On Optimal Early Stopping: Over-informative versus Under-informative Parametrization,” arXiv preprint arXiv:2202.09885, 2022. (Link)
B. Li, Y. Ma, J. Kutz, and X. Yang, “The Adaptive Spectral Koopman Method for Dynamical Systems,” arXiv preprint arXiv:2202.09501, 2022. (Link)
H. Hassani and A. Javanmard, “The curse of overparameterization in adversarial training: Precise analysis of robust generalization for random features regression,” arXiv preprint arXiv:2201.05149, 2022. (Link)
X. Liu, S. W. Chen, G. V. Nardari, C. Qu, F. C. Ojeda, C. J. Taylor and V. Kumar, "Challenges and opportunities for autonomous micro-uavs in precision agriculture," IEEE Micro 42(1) (2022), pp. 61–68.
K. Zhu, H. Chen, M. Liu, X. Tang, W. Shi, N. Sun and D. Z. Pan, "Generative-Adversarial-Network-Guided Well-Aware Placement for Analog Circuits," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
R. S. Rajarathnam, M. B. Alawieh, Z. Jiang, M. Iyer and D. Z. Pan, "DREAMPlaceFPGA: an open-source analytical placer for large scale heterogeneous fpgas using deep-learning toolkit," IEEE/ACM Asian and South Pacific Design Automation Conf. (ASP-DAC), January 2022.
A. F. Budak, Z. Jiang, K. Zhu, A. Mirhoseini and D. Z. Pan, “Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
K. Zhu, H. Chen, M. Liu and D. Z. Pan, “Automating Analog Constraint Extraction: From Heuristics to Learning,” IEEE/ACM Asia and South Pacific Design Automation Conf. (ASP-DAC), January, 2022. (Invited Paper)
S. W. Chen, T. Wang, N. Atanasov, V. Kumar and M. Morari, "Large scale model predictive control with neural networks and primal active sets," Automatica 135 (2022), p. 109947.
V. Keswani, O. Mangoubi, S. Sachdeva and N. K. Vishnoi, "A Convergent and Dimension-Independent First-Order Algorithm for Min-Max Optimization," axXiv preprint, 2021. Intl. Conf. on Machine Learning, 2022. (Link)
E. McCarty, Q. Zhao, A. Sidiropoulos and Y. Wang, "NN-Baker: A neural-network infused algorithmic framework for optimization problems on geometric intersection graphs," Conf. on Neural Information Processing Systems, 2021, to appear.
N. Hansen, H. Su and X. Wang, "Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation," Conf. on Neural Information Processing Systems, 2021.
M. Hao, Y. Li, Z. Di, N. B. Gundavarapu and X. Wang, "Test-Time Personalization with a Transformer for Human Pose Estimation," Conf. on Neural Information Processing Systems, 2021.
J. Wang, H. Xu, M. Narasimhan and X. Wang, "Multi-Person 3D Motion Prediction with Multi-Range Transformers," Conf. on Neural Information Processing Systems, 2021.
A. Jadbabaie, H. Mania, D. Shah and Suvrit Sra, "Time varying regression with hidden linear dynamics," arXiv:2112.14862. (Preprint) (Link)
S. Sra, "Positive definite functions of noncommuting contractions, Hua-Bellman matrices, and a new distance metric," arXiv:2112.00056. (Preprint) (Link)
J. Chen, I. H.-R. Jiang, J. Jung, A. B. Kahng, S. Kim, V. N. Kravets, Y.-L. Li, R. Varadarajan and M. Woo, “DATC RDF-2021: Design Flow and Beyond,” Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-6. (Invited Paper)
J. Jung, A. B. Kahng, S. Kim and R. Varadarajan, "METRICS2.1 and Flow Tuning in the IEEE CEDA Robust Design Flow and OpenROAD," Proc. ACM/IEEE Intl. Conf. on Computer-Aided Design, 2021, pp. 1-9. (Invited Paper) (Link)
C.-K. Cheng, A. B. Kahng, I. Kang, M. Kim, D. Lee, B. Lin, D. Park and M. Woo, "CoRe-ECO: Concurrent Refinement of Detailed Place-and-Route for an Efficient ECO Automation," Proc. ACM/IEEE Intl. Conf. on Computer Design, 2021, pp. 366-373. (Link)
M. Rapp, H. Amrouch, Y. Lin, B. Yu, D. Z. Pan, M. Wolf and J. Henkel, "MLCAD: A Survey of Research in Machine Learning for CAD," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021. (Keynote Paper)
Y. Freund, Y.-A. Ma and T. Zhang, "When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint," (preprint 2021, will acknowledge NSF CCF-2112665 in publication).

Additional Publications

C. Liu, L. Zhu and M. Belkin, "Loss landscapes and optimization in over-parameterized non-linear systems and neural networks," Applied and Computational Harmonic Analysis, 2022. (Link)
H. Chen, C. Fu, J. Zhao and F. Koushanfar, "GALU: A Genetic Algorithm Framework for Logic Unlocking," Digital Threats: Research and Practice, 2021.
W. Mou, Y.-A. Ma, M. J. Wainwright, P. L. Bartlett and M. I. Jordan, "High-order Langevin diffusion yields an accelerated MCMC algorithm," Journal of Machine Learning Research (JMLR), 2021. (Link)
V. Gandikota, A. Mazumdar and S. Pal, "Support Recovery of Sparse Signals from a Mixture of Linear Measurements," Conf. on Neural Information Processing Systems, 2021.
J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka and S. Sra, "Can contrastive learning avoid shortcut solutions?" Conf. on Neural Information Processing Systems, 2021.
A. Ghosh, R. K. Maity, S. Kadhe, A. Mazumdar and K. Ramchandran, "Communication-Efficient and Byzantine-Robust Distributed Learning With Error Feedback," IEEE Journal on Selected Areas in Information Theory, 2021, pp. 942-953.
M. Belkin, "Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation," Acta Numerica, Cambridge University Press, 2021, pp. 203-248. (Link)
M. Shan, Q. Feng, Y. Jau and N. Atanasov, "ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description," IEEE/CVF Intl. Conf. on Computer Vision (ICCV), 2021. (Link)
T. Duong and N. Atanasov, "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control," Robotics: Science and Systems (RSS), 2021. (Link)
Y. Lin, Z. Jiang, J. Gu, W. Li, S. Dhar, H. Ren, B. Khailany and D. Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, pp. 748-761. (Link)

Proposer’s Name	Corresponding Email	Project Title	Brief Description (what is sought)	List of Existing Collaborators (one per line)	List of References	Additional Information	Created
Andrew Kahng	abk-tilos@eng.ucsd.edu	Identifying "doomed runs" of an IC routing tool	The “detailed routing” step of IC place-and-route can take many hours or days. The routing tool must construct a set of trees of wire segments, where each tree connects a prescribed set of terminals of logic gates in the chip design. The trees cannot touch each other. Routing tools perform many iterations of “ripup-and-reroute” to gradually reduce the number of design rule violations in the routing solution. But if the tool cannot reduce the violations to a usable level (e.g., 2000 violations can be fixed by an expert engineer in a week), then the run is useless. This project will try to predict “doomed runs” (or, a final number of violations) based on information available at the initial iterations of the routing tool. Being able to terminate “doomed runs”, without terminating runs that will eventually succeed, has very high value in the chip design process.	Dr. Seungwon Kim, sek006@eng.ucsd.edu	(1) Slides 10, 11, 12 in: https://vlsicad.ucsd.edu/NEWS18/malenda-kahng-talk-distributed.pdf (workshop talk at DAC-2018) (2) GitHub: TritonRoute https://github.com/The-OpenROAD-Project/OpenROAD/tree/master/src/drt (3) A. B. Kahng, L. Wang and B. Xu, "TritonRoute-WXL: The Open Source Router with Integrated DRC Engine", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2021), doi:10.1109/TCAD.2021.3079268 https://vlsicad.ucsd.edu/Publications/Journals/j136.pdf	Google Doc open for comments (please use @abk-tilos@eng.ucsd.edu to flag for attention): https://docs.google.com/document/d/1rOHx6fYesH-XgKHKyNZcWy30lF33WvAJEEJKFBwZBj0/edit?usp=sharing	03/09/2022
Tara Javidi	tjavidi@eng.ucsd.edu	Empirical Optimization of Band-limitted (Graph) Signals For Networks	We have developed an adaptive sampling method for empirical (zeroth order) optimization of a function in reproducing kernel Hilbert space with a known kernel. We are interested in extending this work in the following directions: 1. A special case of RKHS is that of band-limited functions where our algorithm can be precisely compared to a Shannon/Nyquest sampler. This view suggests similar correspondence might be of interest when the input space consists of graphs. 2. Practically, data needs to be used to identify the kernel and estimate parameters. I am interested to work with networking and chip use case folks to see how such estimation can be optimized.	Tara Javidi Farinaz Koushanfar	Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS. https://arxiv.org/abs/2005.04832 AdaNS: Adaptive Non-Uniform Sampling for Automated Design of Compact DNNs		03/11/2022
Andrew Kahng	abk-tilos@eng.ucsd.edu	Learning Good Hierarchical Clustering in Clock Distribution	The clock signal is the “heartbeat” of any synchronous digital circuit. The clock signal is distributed from a “clock source” to every “clock sink” (flip-flop) via a rooted “clock tree” of wires and buffers. For figures, see the reference Doc. (1) The clock signal should reach every sink at roughly the same time: this is the goal of “zero skew” or “bounded skew” (e.g., Charikar et al., SODA99). (2) Also, the clock tree should have minimum total wirelength, since more wirelength means more power consumption. (3) And, the clock signal must be “refreshed” every so often, because a logic gate can only drive a limited amount of capacitive load. Thus, the clock tree has a hierarchy of “clock buffers”, each of which sends the signal onward to a bounded amount of wiring and child buffers/sinks. The goal is to learn how to hierarchically cluster sinks and buffers in clock trees, based on “good example solutions” obtained by running clock tree synthesis (CTS) in commercial place-and-route tools. Benchmark testcases and solution quality evaluators, and potentially relevant metadata/features, can be provided. See the “2013_CTS_study” link in the reference Doc.	Ravi Varadarajan, rvaradarajan@ucsd.edu Matt Liberty, mliberty@eng.ucsd.edu	M. Charikar, J. Kleinberg, R. Kumar, S. Rajagopalan, A. Sahai and A. Tomkins, “Minimizing Wirelength in Zero and Bounded Skew Clock Trees”, Proc. SODA, 1999, pp. 177-184. http://web.cs.ucla.edu/~sahai/work/web/1999Publications/SODA1999.pdf A. Z. Zelikovsky and I. I. Mandoiu, “Practical Approximation Algorithms for Zero- and Bounded-Skew Trees”, https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.1047&rep=rep1&type=pdf See images in HTML files linked from https://vlsicad.ucsd.edu/2013_CTS_study/index.html . E.g., “Click to change level” in https://vlsicad.ucsd.edu/2013_CTS_study/uni_high_ar2.html . Can we learn this clustering, and what vertex or layout features determine this clustering?	Google Doc open for comments (please use @abk-tilos@eng.ucsd.edu to flag for attention): https://docs.google.com/document/d/1_AQNf6N75fz6cBtoQSX3hJsQNJV20DHmTIrZO9NkCvQ/edit?usp=sharing	03/13/2022
Andrew Kahng	abk-tilos@eng.ucsd.edu	Clusters That Stay Together	A chip design can be viewed as a hypergraph with lots of metadata/labels on vertices and hyperedges. A complex sequence of heuristic algorithms will place this hypergraph onto the layout region of the chip. The goal is to reduce problem size without losing solution quality by clustering the hypergraph, such that each cluster “stays together” in the placement. I.e., we want to learn how to cluster “with no mistakes”. Even a 10X problem size reduction “with no mistakes” would have a huge impact on chip design practice. See Slide 6 of the first reference. See also the “Tutorial on chips + ML” (second reference). The existing collaborators can provide relevant data such as hypergraphs, various metadata on vertices and hyperedges, information from placements returned by a commercial place-and-route tool, etc.	Zhiang Wang, zhw033@ucsd.edu Bodhi Pramanik, bpramanik@eng.ucsd.edu	Slide 6 (see talknotes as well) in this PPTX file from the TILOS Retreat (January 6): https://docs.google.com/presentation/d/17ENPs9KLt4MoHtTRdhaXapNweC6auRlm/edit?usp=sharing&ouid=107509409457975522044&rtpof=true&sd=true Work in Progress: “Tutorial on chips + ML”, https://docs.google.com/document/d/15cr32aB1Q7kawFwNq__DvL0_CkzimXr8BTx4Xk0lFGY/edit?usp=sharing M. Fogaça, A. B. Kahng, E. Monteiro, R. Reis, L. Wang and M. Woo, "On the Superiority of Modularity-Based Clustering for Determining Placement-Relevant Clusters", (.pdf) Integration: The VLSI Journal 74 (2020), pp. 32-44.	Google Doc open for comments (please use @abk-tilos@eng.ucsd.edu to flag for attention): https://docs.google.com/document/d/12RqI4FVKcLNfaZq1wCiac3sm3C-duj3qxHYSvuWSnyE/edit?usp=sharing	03/13/2022
Justin McCarty	justinmccarty@mailerking.xyz	Ubjn	Hello, Wondering if you accept link inserts on existing posts on tilos.ai? How much would you charge for this? Also, do you have any other sites for link inserts? Justin If you'd like to unsubscribe click the following link. https://mailerking.xyz/optout/?site=tilos.ai	Hello, Wondering if you accept link inserts on existing posts on tilos.ai? How much would you charge for this? Also, do you have any other sites for link inserts? Justin If you'd like to unsubscribe click the following link. https://mailerking.xyz/optout/?site=tilos.ai	Hello, Wondering if you accept link inserts on existing posts on tilos.ai? How much would you charge for this? Also, do you have any other sites for link inserts? Justin If you'd like to unsubscribe click the following link. https://mailerking.xyz/optout/?site=tilos.ai	Hello, Wondering if you accept link inserts on existing posts on tilos.ai? How much would you charge for this? Also, do you have any other sites for link inserts? Justin If you'd like to unsubscribe click the following link. https://mailerking.xyz/optout/?site=tilos.ai	12/09/2022

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