Efficient Computing for Robotics and AI

In this talk, we will describe how the joint algorithm and hardware design can be used to reduce energy consumption while delivering real-time and robust performance for applications including deep learning, computer vision, autonomous navigation/exploration, and video/image processing. This talk has a greater emphasis on our robotics work compared to "Efficient Computing for AI and Robotics" which focuses more on our AI work. https://youtu.be/41fScmcjrtk Slides: http://www.rle.mit.edu/eems/wp-content/uploads/2019/04/Efficient-Computing-for-AI-and-Robotics.pdf Works highlighted in this talk include * Y.-H. Chen, T. Krishna, J. Emer, V. Sze, “Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks,” IEEE Journal of Solid-State Circuits (JSSC), ISSCC Special Issue, Vol. 52, No. 1, pp. 127-138, January 2017. [Project website http://eyeriss.mit.edu] * A. Suleiman*, Y.-H. Chen*, J. Emer, V. Sze, “Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision,” IEEE International Symposium of Circuits and Systems (ISCAS), Invited Paper, May 2017. http://www.rle.mit.edu/eems/wp-content/uploads/2017/03/2017_iscas_HOG_CNN.pdf * T.-J. Yang, Y.-H. Chen, V. Sze, “Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. http://eyeriss.mit.edu/energy.html * T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, H. Adam, “NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications,” European Conference on Computer Vision (ECCV), September 2018. https://arxiv.org/abs/1804.03230 *D. Wofk*, F. Ma*, T.-J. Yang, S. Karaman, V. Sze, “FastDepth: Fast Monocular Depth Estimation on Embedded Systems,” IEEE International Conference on Robotics and Automation (ICRA), May 2019. http://fastdepth.mit.edu/ *Y.-H. Chen, T.-J Yang, J. Emer, V. Sze, “Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices,” to appear in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), June 2019. https://arxiv.org/abs/1807.07928 * A. Suleiman, Z. Zhang, L. Carlone, S. Karaman, V. Sze, “Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones,” IEEE Journal of Solid-State Circuits (JSSC), VLSI Symposia Special Issue, Vol. 54, No. 4, pp. 1106-1119, April 2019. [Project website http://navion.mit.edu] * Z. Zhang, T. Henderson, S. Karaman, V. Sze, “FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping,” arXiv, May 2019. http://arxiv.org/abs/1905.02238 * P. Z. X. Li*, Z. Zhang*, S. Karaman, V. Sze, “High-throughput Computation of Shannon Mutual Information on Chip,” Robotics: Science and Systems (RSS), June 2019. http://www.rle.mit.edu/eems/wp-content/uploads/2019/05/2019_rss_fsmi_hardware.pdf * J. Noraky, V. Sze, “Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging,” to appear in IEEE Transactions on Circuits and Systems for Video Technology (TCSVT). http://www.rle.mit.edu/eems/wp-content/uploads/2019/03/2019_tcsvt_tof.pdf