Tianqi Tang

Papers

Total Citations

1,260

H-Index

About

Tianqi Tang is a leading researcher at the intersection of efficient deep learning and embedded systems, with a primary focus on deploying convolutional neural networks (CNNs) on resource-constrained hardware platforms. His seminal work, "Going Deeper with Embedded FPGA Platform for Convolutional Neural Network" (2016), has amassed over 1,260 citations, establishing a foundational framework for accelerating deep neural networks on FPGAs. This contribution directly addressed the critical challenge of making computationally intensive CNNs feasible for real-time, low-power applications, bridging the gap between algorithmic advances in computer vision and practical deployment in edge devices. Tang’s research has profoundly influenced the design of hardware-software co-optimization strategies, enabling efficient inference without sacrificing model accuracy. His achievements are widely recognized in the circuits and systems community, where his work has become a standard reference for embedded deep learning. By pioneering methods to compress and accelerate neural networks on reconfigurable logic, Tang has empowered a new generation of intelligent, autonomous systems—from drones to medical imaging devices—demonstrating how cutting-edge AI can be made both powerful and practical for the real world.