Talk Abstract:  Imaging and computing, which acquire and interpret visual data, respectively, are traditionally designed in isolation and simply stitched together in a system, resulting in a sub-optimal whole. This talk discusses how we must rethink the imaging-computing interface and co-design the two to deliver significant efficiency gains.

In particular, I will focus on the paradigm of Computational CMOS Image Sensors (CCIS), where image sensors are equipped with compute capabilities to unlock new applications and reduce energy consumption. Unleashing the power of CCIS, however, requires making a myriad of interlocked design decisions, e.g., computing inside vs. off sensors, 2D vs. 3D stacking, analog vs. digital computing. I will describe a framework we recently developed, validated against real silicon, that models energy consumption of CCIS and, thus, empowers designers to explore architectural design decisions at an early stage. I will describe a few concrete use-cases of our framework in the context of AR/VR. If time permits, I will conclude by discussing some of our recent efforts to co-design optics, image sensors, and machine vision algorithms.

Speaker Biography:  Yuhao Zhu is an Assistant Professor of Computer Science at University of Rochester. He holds a Ph.D. from The University of Texas at Austin and was a visiting researcher at Harvard University and Arm Research. His research group focuses on applications, algorithms, and systems for visual computing. His work is recognized by the Honorable Mention of the 2018 ACM SIGARCH/IEEE-CS TCCA Outstanding Dissertation Award, multiple IEEE Micro Top Picks designations, and multiple best paper awards/nominations in computer architecture, Virtual Reality, and visualization.

More about his research can be found at: http://www.horizon-lab.org/.