Addressing the challenge of slow inference time is crucial for making transformer-based models more practical and accessible for real-world applications like autonomous vehicles and Social robots.
The latest research led by Mr. Apoorv Singh, a Machine Learning Scientist at an autonomous vehicle company, enabled such a computation-hungry transformer model to be deployed in real-time on autonomous cars. This research involves multiple practical and theoretical hypotheses that scaled down the inference time of the Transformers-based Computer Vision model by 63%, keeping a similar detection performance. His research was published at the Autonomous Driving session of the IEEE CVPR conference, held in Vancouver, Canada, in July 2023. Mr. Singh, a robotics graduate from Carnegie Mellon University, has also published multiple patents and research articles in Artificial Intelligence and Autonomous Vehicles convergence. Mr. Singh has represented autonomous vehicle research as a keynote speaker and panelist at US-based IEEE conferences.