Edge AI is going to play a significant role in many areas such as automotive, smart home, smart cities, education, robotics, and surveillance, to name a few. The past few years have seen a rise in the number of HW options designed for accelerating AI inference at the edge. These multiple HW options, however, have made application development for the edge complicated. Each HW option comes with its own inference runtime, model porting SW, operator support, optimizations, and model zoo making it a time consuming effort to evaluate the HW. Performance metrics are not standardized across HW options and even for a fixed HW, they vary depending on the model and the host system. All these factors make evaluating edge HW a challenging task. In this talk, we will provide an overview of these challenges as well as our attempts to alleviate these problems.
Shashi Kiran Chilappagari
Shashi Chilappagari is the Co-Founder and Chief Architect at DeGirum Corp., a fabless semiconductor company building complete AI solutions for the edge. Prior to DeGirum, he was the Director of SSD Architecture at Marvell Semiconductor Inc. Shashi has B. Tech and M. Tech degrees from Indian Institute of Technology, Madras, India and Ph.D. from the University of Arizona, Tucson, Arizona.