Talks and Tutorials
MIT Professional Education Short Course on Designing Efficient Deep Learning Systems
Videos of talks and demos can be found on our YouTube channel.
- V. Sze, “Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design,” Cornell ECE Colloquium, presented on September 25, 2023 [Slides]
- D. Niño, V. Sze, “Building Skills for a Successful PhD,” IAP Workshop, presented on January 31, 2023 [ Slides ]
- V. Sze, “Efficient Computing for Autonomy and Navigation,” University of Toronto Robotics Institute, presented on March 15, 2022 [ Slides | Video ]
- V. Sze, “Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design,” Keynote at 2021 IEEE International Symposium on Circuits and Systems (ISCAS), presented on May 26, 2021 [ Slides ]
- V. Sze, “Efficient Computing for AI and Robotics: From Hardware Accelerators to Algorithm Design,” UW-Madison Virtual Computer Architecture Seminar for Fall 2020, presented on September 29, 2020 [ Slides | Video ]
- V. Sze, “Reducing the Carbon Emissions of ML Computing - Challenges and Opportunities,” Frontiers in Machine Learning 2020, presented on July 23, 2020 [ Slides | Video ]
- V. Sze, “Efficient Computing For Low-Energy Robotics,” Purdue ECE Seminar, presented on July 16, 2020 [ Slides ]
- V. Sze, “How to Evaluate Efficient Deep Neural Network Approaches,” Workshop on Efficient Deep Learning in Computer Vision at CVPR 2020, presented on June 15, 2020 [ Slides ]
- V. Sze, “The Intersection of SSCS and AI — A Tale of Two Journeys,” SSCS and tinyML Webinar, presented on May 26, 2020 [ Slides | Video ]
- V. Sze, “How to Evaluate Deep Neural Network Accelerators,” Workshop on On-Device Intelligence at MLSys 2020, presented on March 4, 2020 [ Slides | Video ]
- V. Sze, “Efficient Computing for AI and Robotics,” MTL Seminar Series, presented on February 26, 2020 [ Slides ]
- V. Sze, “How to Understand and Evaluate Deep Learning Processors,” International Solid-State Circuits Conference, presented on February 16, 2020 [ Slides ]
- V. Sze, “Efficient Computing for Deep Learning, AI and Robotics,” Deep Learning Lecture Series, presented on January 15, 2020 [ Slides ]
- V. Sze, “Co-Design Approaches for Efficient Deep Neural Networks: Challenges and Opportunities,” Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019, presented on December 13, 2019 [ Slides ]
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- V. Sze, “Efficient Processing of Deep Neural Networks: from Algorithms to Hardware Architectures,” Conference on Neural Information Processing Systems (NeurIPS), Invited Tutorial, presented on December 9, 2019 [ Slides | Video ]
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- V. Sze, T.-J. Yang, “Efficient Image Processing with Deep Neural Networks,” IEEE International Conference on Image Processing (ICIP), presented on September 22, 2019 [ Slides ]
- V. Sze, “Domain-Specific Architectures for AI and Robotics: Opportunities and Challenges,” SIGARCH Visioning Workshop on Agile and Open Hardware for Next-Generation Computing at ISCA 2019, presented on June 23, 2019 [ Slides ]
- V. Sze, “Balancing Efficiency and Flexibility for DNN Acceleration,” EMC^2: Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications
- V. Sze, “Exploiting Redundancy for Efficient Processing of DNNs and Beyond,” Coding Theory for Large-Scale Machine Learning at ICML 2019, presented on June 15, 2019 [ Slides | Video ]
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- V. Sze, “Understanding the Challenges of Algorithm and Hardware Co-design for Deep Neural Networks,” Invited talk at Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR) at ICML 2019, presented on June 14, 2019 [ Slides | Video ]
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- V. Sze, “Energy-Efficient AI,” TEDxMIT, presented on May 28, 2019 [ Slides ]
- V. Sze, “Efficient Computing for Robotics and AI,” ESE Spring Colloquia at University of Pennsylvania, presented on May 9, 2019 [ Slides ]
- V. Sze, “Efficient Computing for AI and Robotics” [ Slides ] presented at
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- Xilinx Emerging Technology Symposium (April 11, 2019)
- Stanford SystemX (April 11, 2019)
- Berkeley Wireless Research Center (April 12, 2019)
- V. Sze, “Efficient Computing for Autonomous Navigation of Miniaturized Robots,” MARS Conference 2019, presented on March 19, 2019 [ Slides ]
- V. Sze, “Energy-Efficient AI,” MIT College of Computing Kickoff, presented on February 28, 2019 [ Slides ]
- V. Sze, “Energy-Efficient Edge Computing for AI-driven Applications,” Distinguished Lecture at University of Toronto, presented on November 22, 2018 [ Slides ]
- V. Sze, “Energy-Efficient Edge Computing for AI-driven Applications,” Keynote at 2018 International Conference on Field-Programmable Logic and Applications (FPL), presented on August 27, 2018 [ Slides ]
- A. Suleiman, Z. Zhang, L. Carlone, S. Karaman, V. Sze, “Navion: An Energy-Efficient Visual-Inertial Odometry Accelerator for Micro Robotics and Beyond,” IEEE Hot Chips: A Symposium for High-Performance Chips, August 2018. [ Slides ] Highlighted in EETimes
- V. Sze, “Energy-Efficient Processing at the Edge: From Compressing to Understanding Pixels,” Keynote at 2018 Picture Coding Symposium, presented on June 25, 2018 [ Slides ]
- V. Sze, “Hardware for Machine Learning: Design Considerations,” Symposia on VLSI Technology and Circuits, Forum on Machine Learning Today and Tomorrow: Technology, Circuits and System View, presented on June 22, 2018 [ Slides ]
- V. Sze, “Energy-Efficient Deep Learning: Challenges and Opportunities,” IEEE/SSCS IEEE Distinguished Lecture/Webinar, presented on April 10, 2018 [ Slides ]
- V. Sze, “Understanding the Limitations of Existing Energy-Efficient Design Approaches for Deep Neural Networks,” Forum on The Next Waves of Machine and Deep Learning Hardware at CICC 2018, presented on April 9, 2018 [ Slides ]
- V. Sze, “Energy-Efficient Edge Computing for AI-driven Applications,” Keynote at 2018 UIUC CSL Student Conference, presented on February 22, 2018 [ Slides ]
- V. Sze, “Efficient Edge Solutions for Deep Learning Applications,” Short Course on Hardware Approaches to Machine Learning and Inference at ISSCC 2018, presented on February 15, 2018 [ Slides ]
- V. Sze, “Efficient Processing for Deep Learning,” Embedded Vision Webinar, presented on September 28, 2017 [ Slides ]
- “Tutorial on Hardware Architectures of Deep Neural Networks,” ISCA 2017, presented on June 24, 2017 (also appeared at MICRO-49) [ Website ]
- Background of Deep Neural Networks [ slides ]
- Survey of DNN Development Resources [ slides ]
- Survey of DNN Hardware [ slides ]
- DNN Accelerator Architectures [ slides ]
- Advanced Technology Opportunities [ slides ]
- Network and Hardware Co-Design [ slides ]
- Benchmarking Metrics [ slides ]
- Summary [ slides ]
- References [ slides ]
- Entire Tutorial [ slides ]
- V. Sze, “What If Your Smart Phone Didn’t Need The Cloud?,” MIT ILP Europe Conference in Vienna, presented on March 29, 2017.
- V. Sze, “Joint Design of Algorithms and Hardware for Energy-Efficient DNNs,” NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks, presented on December 9, 2016. [ Slides ]
- V. Sze, Y.H. Chen, “Building Energy-Efficient Accelerators for Deep Learning,” Deep Learning Summit Boston – RE•WORK, presented on May 12, 2017. [ Slides ]
- V. Sze, “Energy-Efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks,” [ Slides ] presented at
- ICML 2016 Workshop on On-Device Intelligence (June 24, 2016)
- CVPR 2016 Embedded Vision Workshop (July 1, 2016)
- Embedded Vision Alliance (September 20, 2016)
- ICCAD 2016 Workshop on Hardware and Algorithms for Learning On-a-chip (November 10, 2016)
- V. Sze, M. Budagavi, “Design and Implementation of Next Generation Video Coding Systems (H.265/HEVC Tutorial),” IEEE International Symposium on Circuits and Systems (ISCAS), presented on June 1, 2014. [ Slides ]
