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About

Jiantao Qiu is a researcher working at the intersection of hardware acceleration, embedded systems, and autonomous robotics, with particular expertise in deploying deep learning efficiently on resource-constrained platforms. His most influential contribution, "Going Deeper with Embedded FPGA Platform for Convolutional Neural Network" (2016), has garnered over 1,260 citations and stands as a landmark work in the field, demonstrating how convolutional neural networks can be effectively implemented on FPGA hardware — a critical challenge as AI moves from data centers to edge devices. This work has shaped how the community approaches efficient CNN deployment in embedded systems. Beyond hardware acceleration, Qiu has expanded his research into autonomous multi-robot exploration, developing benchmark tools through Explore-Bench (2022) to standardize evaluation of both frontier-based and reinforcement-learning-driven exploration strategies. His INCA and INCAME accelerator series further bridges his dual interests, designing interruptible CNN accelerators tailored for real-world multi-robot systems where competing computational tasks must be managed gracefully. He has also contributed to sparse-reward reinforcement learning through his LiFE exploration framework. Collectively, Qiu's work reflects a coherent vision: making intelligent, autonomous systems both computationally feasible and practically deployable in the real world.