RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning

Rishabh Tiwari, Arnav Chavan, Deepak Gupta, Gowreesh Mago, Animesh Gupta, Akash Gupta, Suraj Sharan, Yukun Yang, Shanwei Zhao, Shihao Wang, Youngjun Kwak, Seonghun Jeong, Yunseung Lee, Changick Kim, Subin Kim, Ganzorig Gankhuyag, Ho Jung, Junwhan Ryu, HaeMoon Kim, Byeong Hak Kim et al. (18 additional authors not shown)

Abstract: This paper delves into the results of two resource-constrained deep learning challenges, part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at ICCV 2023, focusing on memory and time limitations. The challenges garnered significant global participation and showcased a range of intriguing solutions. The paper outlines the problem statements for both tracks, summarizes baseline and top-performing approaches, and provides a detailed analysis of the methods used. While the presented solutions constitute promising initial progress, they represent the beginning of efforts needed to address this complex issue. We conclude by emphasizing the importance of sustained research efforts to fully address the challenges of resource-constrained deep learning.