Kurt Keutzer

Research Areas

Research Centers

Teaching Schedule

Spring 2025

Biography

Kurt received his Ph.D. degree in Computer Science from Indiana University in 1984 and then joined the research division of AT&T Bell Laboratories. In 1991 he joined Synopsys, Inc. where he ultimately became Chief Technical Officer and Senior Vice-President of Research. In 1998 Kurt became Professor of Electrical Engineering and Computer Science at the University of California at Berkeley. Kurt’s research now focuses on systems issues associated with the application of Deep Learning to computer vision, speech recognition, natural language processing, and finance.

Kurt has published six books, over 250 refereed articles, and is among the most highly cited authors in Hardware and Design Automation. Kurt was elected a Fellow of the IEEE in 1996. At the 50th Design Automation Conference Kurt received a number of awards reflecting achievements over the 50 year history of the conference. These included “Top Ten Cited Author” and “Top Ten Cited Paper.” He was also recognized as among one of only three people to have received four Best Paper Awards in the history of the conference. Kurt's research on Deep Learning has also received Best Paper Awards at the Embedded Vision Workshop and at the International Conference on Parallel Processing.

As an entrepreneur Kurt has served as an angel investor and advisor to over twenty-five start-up companies including C-Cube Microsystems, Coverity, Simplex, and Tensilica. Kurt co-founded DeepScale with his PhD student Forrest Iandola. DeepScale was acquired by Tesla in 2019.

Education

  • 1984, PhD, Computer Science, Indiana University

Selected Publications

  • S. Shen, Z. Yao, A. Gholami, M. Mahoney, and K. Keutzer, "Powernorm: Rethinking batch normalization in transformers," in International Conference on Machine Learning, 2020, pp. 8741--8751.
  • Y. Cai, Z. Yao, Z. Dong, A. Gholami, M. W. Mahoney, and K. Keutzer, "Zeroq: A novel zero shot quantization framework," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13169--13178.
  • Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, S. Bhojanapalli, X. Song, J. Demmel, K. Keutzer, and C. Hsieh, "Large batch optimization for deep learning: Training bert in 76 minutes," in International Conference on Learning Representations, 2020.
  • P. Jain, A. Jain, A. Nrusimha, A. Gholami, P. Abbeel, K. Keutzer, I. Stoica, and J. Gonzalez, "Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization," in Proceedings of Machine Learning and Systems 2020, Machine Learning and Systems, 2020, pp. 497--511.
  • S. Shen, Z. Dong, J. Ye, L. Ma, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer, "Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT.," in AAAI, 2020, pp. 8815--8821.
  • S. Zhao, B. Li, X. Yue, Y. Gu, P. Xu, R. Hu, H. Chai, and K. Keutzer, "Multi-source domain adaptation for semantic segmentation," in Advances in Neural Information Processing Systems, 2019, pp. 7287--7300.
  • Y. You, Z. Zhang, C. Hsieh, J. Demmel, and K. Keutzer, "Fast deep neural network training on distributed systems and cloud TPUs," IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 11, pp. 2449--2462, Nov. 2019.
  • X. Yue, Y. Zhang, S. Zhao, A. L. Sangiovanni-Vincentelli, K. Keutzer, and B. Gong, "Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data," in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 2100--2110.
  • Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer, "Hawq: Hessian aware quantization of neural networks with mixed-precision," in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 293--302.
  • Z. Yao, A. Gholami, P. Xu, K. Keutzer, and M. W. Mahoney, "Trust region based adversarial attack on neural networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 11350--11359.
  • B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer, "Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 10734--10742.
  • B. Wu, X. Zhou, S. Zhao, X. Yue, and K. Keutzer, "Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud," in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 4376--4382.
  • A. Gholami, K. Kwon, B. Wu, Z. Tai, X. Yue, P. Jin, S. Zhao, and K. Keutzer, "Squeezenext: Hardware-aware neural network design," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1638--1647.
  • Y. You, Z. Zhang, C. Hsieh, J. Demmel, and K. Keutzer, "Imagenet training in minutes," in Proceedings of the 47th International Conference on Parallel Processing, 2018, pp. 1--10.
  • A. Gholami, A. Azad, P. Jin, K. Keutzer, and A. Buluç, "Integrated Model, Batch, and Domain Parallelism in Training Neural Networks," in SPAA'18: 30th ACM Symposium on Parallelism in Algorithms and Architectures, 2018.
  • B. Wu, A. Wan, X. Yue, P. Jin, S. Zhao, N. Golmant, A. Gholaminejad, J. Gonzalez, and K. Keutzer, "Shift: A zero flop, zero parameter alternative to spatial convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9127--9135.
  • B. Wu, A. Wan, X. Yue, and K. Keutzer, "Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud," in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 1887--1893.
  • F. Iandola and K. Keutzer, "small neural nets are beautiful: enabling embedded systems with small deep-neural-network architectures," in 2017 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS), 2017, pp. 1--10.
  • B. Wu, F. Iandola, P. H. Jin, and K. Keutzer, "Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 129--137.
  • F. N. Iandola, M. W. Moskewicz, K. Ashraf, and K. Keutzer, "Firecaffe: near-linear acceleration of deep neural network training on compute clusters," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2592--2600.

Awards, Memberships and Fellowships