About Tony
MY BACKGROUND
Dr. Tony Geng is a tenure-track assistant professor in the ECE and CS departments of the University of Rochester (UR) and the director of UR's IntelliArch (Intelligent Architecture) Lab. He also holds secondary appointments with the Goergen Institute for Data Science. Before joining Rochester, Tony worked in the Physical & Computational Sciences Directorate at Pacific Northwest National Laboratory (PNNL), US Department of Energy. His research interests are at the intersection of Computer Architecture & Systems, Generative AI, and AI for Scientific Exploration. Tony's papers have appeared in many prestigious conferences and journals e.g. ISCA, MICRO, HPCA, OSDI, ICLR, ICML, NeurIPS, CVPR, ICCV, AAAI, DAC, SC, TPDS, TC, and TIP.
Job Openings:
Prof. Tony Geng will join Rice University in the 2025–2026 academic year and will no longer admit Ph.D. students at the University of Rochester. Interested applicants should apply to Rice Ph.D. programs.
I am seeking two Ph.D. students and two postdoctoral researchers to advance Generative AI paradigms and their underlying computing systems through co-design across their algorithms, hardware architecture, and applications.
RESEARCH INTERESTS
Computer Architecture: Accelerators for GenAI, GPU, FPGA, Brain-Inspired Computer
Machine Learning: Generative AI (LLM, Diffusion Model), Brain-Inspired AI, Physics-Informed AI
Applications: Fintech, Social Media, Recommendation System, Scientific Discovery
Selected Publications
2025:
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[ISCA 2025] C.Wu*, R.Song*, C.Liu, P.Haghi, A.Li, M.Huang, T.Geng: "DS-TPU: Dynamical System for on-Device Lifelong Graph Learning with Nonlinear Node Interaction", the 52nd IEEE/ACM International Symposium on Computer Architecture.
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[MICRO 2025] C.Liu, C.Wu, R.Song, G.Sun, Y.Wu, Y.Chen, A.Li, T.Geng: "DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving", 58th IEEE/ACM International Symposium on Microarchitecture.
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[DAC 2025] P.Haghi, A.Falahati, Z.Azad, C.Wu, R.Song, C.Liu, A.Li, T.Geng: "DM-Tune: Quantizing Diffusion Models with Mixture-of-Gaussian Guided Noise Tuning", The 61st Design Automation Conference.
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[ICML 2025] C.Liu, C.Wu, R.Song, A.Li, Y.Wu, T.Geng: "An Expressive and Self-Adaptive Dynamical System for Efficient Equation Learning", Forty-third International Conference on Machine Learning.
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[ICLR 2025] C.Liu, R.Song, C.Wu, P.Haghi, T.Geng: "InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems", The Thirteenth International Conference on Learning Representations.
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[ICLR 2025] C.Liu, C.Wu, D.Liu, Y.Wu, T.Geng: "Beyond-Moore LLM: Uncovering a New Type of Compute Resource for LLM Training with Revolutionary Computing Efficiency", The Thirteenth International Conference on Learning Representations.
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[ICLR 2025] C.Liu, C.Wu, S.Cao, M.Chen, J.Liang, A.Li, M.Huang, C.Ren, Y.Wu, D.Liu, T.Geng: "Revolutionizing Scientific Simulation with Diffusion Models", The Thirteenth International Conference on Learning Representations.
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[ICLR 2025] G.Sun, M.Jin, Z.Wang, C.Wang, S.Ma, Q.Wang, T.Geng, Y.Wu, Y.Zhang, D.Liu: "Visual Agents as Fast and Slow Thinkers", The Thirteenth International Conference on Learning Representations.
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[NeurIPS 2025] R.Zeng, J.Liang, C.Han, Z.Cao, J.Liu, X.Quan, Y.Chen, L.Huang, T.Geng, Q.Wang, D.Liu: "Probabilistic Token Alignment for Large Language Model Fusion", The Thirty-ninth Annual Conference on Neural Information Processing Systems.
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[ATC 2025] Y.Wang, B.Feng, Z.Wang, T.Geng, A.Li, Y.Ding: "GMI-DRL: Empowering Multi-GPU DRL with Adaptive-Grained Parallelism", 2025 USENIX Annual Technical Conference.
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[EMNLP 2025] R.Zeng, G.Sun, Q.Wang, ..., L.Huang, D.Liu: "MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper", the 2025 Conference on Empirical Methods in Natural Language Processing. (ORAL)
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[ICS 2025] A. Guo, Y. Hao, X. Yao, S. Yang, J. Huang, T. Geng, M. Herbordt: "SmartNIC-GPU-CPU Heterogeneous System for Large Machine Learning Model with Software-Hardware Codesign", the 39th ACM International Conference on Supercomputing.
​ 2024:
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[ISCA 2024] R.Song*, C.Wu*, C.Liu, A.Li, M.Huang, T.Geng: "DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems", the 51st IEEE/ACM International Symposium on Computer Architecture.
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[MICRO 2024] P.Haghi, C.Wu, Z.Azad, Y.Li, A.Gui, Y.Hao, A.Li, T.Geng: "Bridging the Gap Between LLMs and LNS with Dynamic Data Format and Architecture Codesign", 57th IEEE/ACM International Symposium on Microarchitecture.
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[ICLR 2024] C.Wu, R.Song, C.Liu, Y.Yang, A.Li, M.Huang, T.Geng: "Extending Power of Nature from Binary to Real-valued Graph Learning in the Real World", The Twelfth International Conference on Learning Representations.
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[NeurIPS 2024] R.Zeng, C.Han, Q.Wang, C.Wu, T.Geng, L.Huang, Y.Wu, D.Liu: "Visual Fourier Prompt Tuning", Thirty-eighth Conference on Neural Information Processing Systems.
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[ICML 2024] C.Han, Y.Lu, G.Sun, J.Liang, Z.Cao, Q.Wang, Q.Guan, S.Dianat, R.Rao, T.Geng, Z.Tao, D.Liu "Prototypical Transformer As Unified Motion Learners", Forty-second International Conference on Machine Learning.
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[TC 2024] C.Wu, A.Guo, P.Haghi, A.Li, T.Geng, M.Herbordt: "FPGA-Accelerated Range-Limited Molecular Dynamics", IEEE Transactions on Computers.
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[Briefings in Bioinformatics 2024] Y.Wang, J.Zhao, H.Xu, C.Han, Z.Tao, D.Zhou, T.Geng, D.Liu, Z.Ji: "A systematic evaluation of computational methods for cell segmentation", Briefings in Bioinformatics (Impact Factor: 13.99).
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[AIS 2024] Y.Li, J.Liu, X.Zhao, W.Liu, T.Geng, A.Li, X.Zhang: "Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids", Advanced Intelligent Systems Journal (Impact Factor: 7.4).
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[SC 2024] H.Feng, B.Zhang, F.Ye, M.Si, C.Chu, J.Tian, C.Yin, Z.Deng, Y.Hao, P.Balaji, T.Geng, D.Tao: "Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression", Proceedings of the International Conference for High-Performance Computing, Networking, Storage, and Analysis.
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[ICS 2024] P.Haghi, C.Tan, A.Guo, C.Wu, D.Liu, A.Li, A.Skjellum, T.Geng, M.Herbordt: "SmartFuse: Reconfigurable Smart Switches to Accelerate Fused Collectives in HPC Applications", the 38th ACM International Conference on Supercomputing.
2023:
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[DAC 2023] Z.Liu, Y.Yang, Z.Pan, A.Sharma, A.Hasan, C.Ding, A.Li, M.Huang, T.Geng: "Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning", The 59th Design Automation Conference.
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[DAC 2023] Y.Luo*, C.Tan*, N.Agostini, A.Li, A.Tumeo, N.Dave, T.Geng: "ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs", The 59th Design Automation Conference.
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[AAAI 2023] Z.Pan, A.Sharma, J.Hu, Z.liu, A.Li, H.Liu, M.Huang, T.Geng: "Ising-Traffic: An Ising-based Framework for Traffic Congestion Prediction with Uncertainty", Thirty-Seventh AAAI Conference on Artificial Intelligence.
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[ICS 2023] A.Guo, Y.Hao, C.Wu, P.Haghi, Z.Pan, M.Si, D.Tao, A.Li, M.Herbordt, T.Geng: "Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training", the 36th ACM International Conference on Supercomputing.
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[MICRO 2023] U.Vengalam, Y.Liu, T.Geng, H.Wu, M.Huang: "Supporting Energy-Based Learning With an Ising Machine Substrate: A Case Study on RBM", the 56th IEEE/ACM International Symposium on Microarchitecture.
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[SC 2023] C.Wu, T.Geng, A.Guo, S.Bandara, P.Haghi, C.Liu, A.Li, M.Herbordt: "FASDA: An FPGA-Aided, Scalable and Distributed Accelerator for Range-Limited Molecular Dynamics", Proceedings of the International Conference for High-Performance Computing, Networking, Storage, and Analysis.
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[OSDI 2023] Y.Wang, B.Feng, Z.Wang, T.Geng, A.Li, K.Barker, Y.Ding: "MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms", USENIX Symposium on Operating Systems Design and Implementation.
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[ICS 2023] P.Haghi, W.Krska, C.Tan, T.Geng, ..., A.Li, A.Skjellum, M.Herbordt: "FLASH: FPGA-Accelerated Smart Switches with GCN Case Study", the 36th ACM International Conference on Supercomputing.
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[DAC 2023] H.Peng, ..., C.Wang, T.Geng, W.Wen, X.Xu, C.Ding: "PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment", The 59th Design Automation Conference.
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[NeurIPS 2023] J.Liang, Y.Cui, Q.Wang, T.Geng, W.Wang, D.Liu: "ClusterFomer: Clustering As A Universal Visual Learner", Thirty-seventh Conference on Neural Information Processing Systems.
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[NeurIPS 2023] H.Peng, R.Ran, ..., T.Geng, X.Xu, W.Wen, C.Ding: "LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference", Thirty-seventh Conference on Neural Information Processing Systems.​ ​
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[ICCV 2023] H.Peng, S.Huang, ..., T.Geng, K.Mahmood, W.Wen, X.Xu, C.Ding: "AutoReP: Automatic ReLU Replacement for Fast Private Network Inference", 2023 International Conference on Computer Vision.
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[TIP 2023] D.Liu, J.Liang, T.Geng, A.Loui, T.Zhou: "Tripartite Feature Enhanced Pyramid Network for Dense Prediction", IEEE Transactions on Image Processing (Impact Factor: 10.86).
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[CVPR 2023] Y.Lu, Q.Wang, S.Ma, T.Geng, Y.Chen, H.Chen, D.Liu: "TransFlow: Transformer as Flow Learner", Conference on Computer Vision and Pattern Recognition 2023.
​ 2022:​
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[TPDS 2022] W.Sun, A.Li, T.Geng, S.Stuijk, H.Corporaal: "Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors", IEEE Transactions on Parallel and Distributed Systems.
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[HPCA 2022] H.You*, T.Geng*, Y.Zhang, A.Li, Y.Lin: "GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design", The 28th IEEE International Symposium on HighPerformance Computer Architecture.
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[HPCA 2022] C.Tan, N.B.Agostini, T.Geng, C.Xie, J.Li, A.Li, K.Barker, A.Tumeo: "DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs", The 28th IEEE International Symposium on High-Performance Computer Architecture.
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[DAC 2022] H. Peng, ..., T.Geng, ..., C.Ding: "A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining", The 58th Design Automation Conference.​
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[ICS 2022] C.Zhang, S.Jin, T.Geng, J.Tian, A.Li, D.Tao: "Accelerating Parallel I/O Via Hardware-Algorithm Co-Designed Adaptive Lossy Compression", the 36th ACM International Conference on Supercomputing.
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[ICS 2022] C.Tan, T.Tembe, J.Zhang, B.Fang, T.Geng, G.Wei, D.Brooks, A.Tumeo, G.Gopalakrishnan A.Li: "ASAP - Automatic Synthesis of Area-Efficient and Precision-Aware CGRA", the 36th ACM International Conference on Supercomputing.
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2021:
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[MICRO 2021] T.Geng, C.Wu, ..., M.Herbordt, Y.Lin, A.Li: "I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization", the 54th IEEE/ACM International Symposium on Microarchitecture.
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[TPDS 2021] T.Geng, T.Wang, C.Wu, Y.Li, ..., A.Li, M.Herbordt: "O3BNN-R: An Out-Of-Order Architecture for HighPerformance and Regularized BNN inference", IEEE Transactions on Parallel and Distributed Systems.
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[TPDS 2021] C.Tan, C.Xie, T.Geng, ..., K.Barker, A.Li: "ARENA: Asynchronous Reconfigurable Accelerator Ring to Enable Data-Centric Parallel Computing", IEEE Transactions on Parallel and Distributed Systems.
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[SC 2021] B.Feng, Y.Wang, T.Geng, A.Li, Y.Ding: "APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores", Proceedings of the International Conference for High-Performance Computing, Networking, Storage, and Analysis.
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[ICCAD 2021] Y.Zhang, H.You, Y.Fu, T.Geng, A.Li, Y.Lin: "G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency", 2021 International Conference On Computer Aided Design.
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[ICCD 2021] C.Tan, T.Geng, C.Xie, N.Agostini, J.Li, A.Li, K.Barker, A.Tumeo: "DynPaC: Coarse-Grained, Dynamic, and Partially Reconfigurable Array for Streaming Applications", the 39th IEEE International Conference on Computer Design. (Best Paper Award)
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2020:
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[MICRO 2020] T.Geng, A.Li, T.Wang, C.Wu, Y.Li, ..., M.Herbordt: "AWB-GCN: A Hardware Accelerator of GraphConvolution-Network through Runtime Workload Rebalancing", the 53rd IEEE/ACM International Symposium on Microarchitecture.
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[TC 2020] T.Geng*, T.Wang*, A.Li, X.Jin, M.Herbordt: "FPDeep: Scalable Acceleration of CNN Training on DeeplyPipelined FPGA Clusters", IEEE Transactions on Computers.
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[ICS 2020] T.Geng*, R.Shi*, P.Dong*, ..., M.Herbordt, A.Li, Y.Wang: "CSB-RNN: A Faster-than-Realtime RNN Acceleration Framework with Compressed Structured Blocks", the 34th ACM International Conference on Supercomputing.
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2019:
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[ICS 2019] T.Geng, T.Wang, C.Wu, C.Yang, W.Wu, A.Li, M.Herbordt: "O3BNN: An Out-Of-Order Architecture for High-Performance Binarized Neural Network Inference with Fine-Grained Pruning", the 33th ACM International Conference on Supercomputing.
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[SC 2019] A.Li, T.Geng, T.Wang, M.Herbordt, S.Song, K.Barker: "BSTC: A Novel BinarizedSoft-Tensor-Core Design for Accelerating Bit-Based Approximated Neural Nets", Proceedings of the International Conference for High-Performance Computing, Networking, Storage, and Analysis.
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[SC 2019] C.Yang, T.Geng, T.Wang, ..., M.Herbordt: "Fully integrated FPGA molecular dynamics simulations", Proceedings of the International Conference for High-Performance Computing, Networking, Storage, and Analysis.
Projects
Efficient Generative AI
Pioneering next-generation GenAI through innovative architecture-algorithm co-design, we aim to jointly enhance efficiency and creativity in GenAI inference, pretraining, and fine-tuning, forging a path toward truly efficient AI.
AI for Broadly-Defined Science
Recognizing the soaring expressivity of GenAI, we are pioneering its applications in broadly defined scientific exploration. Our current interests include developing domain LLMs and diffusion models for complex system analysis.
Nature-Powered AI Processors
‘Using Nature as a Low-Power Supercomputer’ is no longer a dream! We are developing new AI processors that harness nature's power and follow the brain's operational principle to perform GenAI computation.
Meet The Team

Ruibing Song

Chuan Liu

Jewelianna Langston

Yu Hu

Shirou Jing
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Guangyan Sun
News
10/2025 Prof. Tony Geng gave an invited talk at CLSAC 2025: "Dynamical-System AI: Beyond the Scaling Crisis, Toward Transformative Efficiency".
08/2025 One paper accepted by NeurIPS 2025.
08/2025 Shirou Jing & Yu Hu joined the big family. Welcome!
08/2025 One paper accepted by EMNLP 2025 for Oral Presentation.
07/2025 One paper accepted by MICRO 2025.
07/2025 Chunshu will join PNNL (US Department of Energy) in July. Big Congrats to Chunshu!!!
04/2025 One paper accepted by ICML 2025.
04/2025 One paper accepted by ATC 2025.
03/2025 One paper accepted by ISCA 2025.
03/2025 One paper accepted by ICS 2025.
02/2025 One paper accepted by DAC 2025.
01/2025 Four papers accepted by ICLR 2025.
01/2025 Guangyan Sun joined the big family. Welcome!
11/2024 One paper accepted by LoG 2024.
10/2024 One paper accepted by ASP-DAC 2024.
09/2024 One paper accepted by NeurIPS 2024.
08/2024 One paper accepted by Briefings in Bioinformatics 2024 - Impact Factor: 13.99.
08/2024 One paper accepted by Advanced Intelligent Systems 2024 - Impact Factor: 7.4.
08/2024 Pouya will join Meta in August. Big Congrats to Pouya!!!
07/2024 One paper accepted by MICRO 2024.
06/2024 One paper accepted by SC 2024.
05/2024 Prof. Tony Geng received the Award for Excellence in Graduate Teaching!
05/2024 One paper accepted by ICML 2024.
04/2024 One paper accepted by ICS 2024.
03/2024 One paper accepted by ISCA 2024.
01/2024 One paper accepted by TC 2024
01/2024 One paper accepted by ICLR 2024: Extending binary Ising machines to real-valued dynamical systems through hardware architecture and Hamiltonian codesign for graph learning problems.
09/2023 Two papers accepted by NeurIPS 2023.
08/2024 Ruibing Song & Chuan Liu joined the big family. Welcome!
07/2023 One paper accepted by MICRO 2023.
07/2023 One paper accepted by ICCAD 2023.
07/2023 One paper accepted by ICCV 2023.
07/2023 One paper accepted by JPCC (Journal of Physical Chemistry C) 2023.
06/2023 One paper accepted by SC 2023.
04/2023 One paper accepted by IEEE Transactions on Image Processing (TIP) 2023 - Impact Factor: 10.86.
04/2023 Two papers accepted by ICS 2023 -- SmartNIC and SmartSwitch can significantly improve DLRM and GNN training efficiency.
03/2023 One paper accepted by OSDI 2023.
02/2023 One paper accepted by CVPR 2023 (as a Highlighted Paper).
02/2023 Three papers accepted by DAC 2023.
11/2022 One paper accepted by AAAI 2023.
10/2022 One paper accepted by TPDS 2022.
​09/2022 Prof. Tony Geng received Faculty Research Award from META (Facebook) on AI System Hardware/Software Codesign.
09/2022 Four papers were accepted by ICCD 2022.
06/2022 Three papers were accepted by FPL 2022.
04/2022 Two papers were accepted by ICS 2022.
02/2022 One paper was accepted by DAC 2022.


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