(pictured L-R: Zhili Xiong, Rachel Selina Rajarathnam, Zhi-Xing Jiang, Hanqing Zhu)

A team of Texas ECE PhD students won first place in the 2023 ACM/IEEE Workshop on Machine Learning for CAD (MLCAD) FPGA Macro Placement Contest. 

The students on the team were Zhili Xiong, Rachel Selina Rajarathnam, Zhi-Xing Jiang, and Hanqing Zhu. They are supervised by Prof. David Z. Pan in the UT Design Automation Laboratory (UTDA).

About the Contest

From IEEE/ACM: "Macro placement plays an integral role in routability and timing closure in both the ASIC and FPGA physical design flows. In particular, the discrete and columnated nature of the FPGA device layout presents unique placement constraints on placeable macros (e.g., BRAM’s, DSP’s, URAM’s, cascaded shapes, etc.). These constraints are challenging for classical optimization and combinatorial approaches, and often the generated floorplans result in netlist design placements with routing and timing closure issues. Inspired by recent deep reinforcement learning (RL) approaches (e.g. [1]), the goal of the competition is to spur academic research for developing ML or deep RL approaches to improve upon the current state-of-the-art macro placement tools."