ART-3D: Analytical 3D Placement with Reinforced Parameter Tuning for Monolithic 3D ICs

Gauthaman Murali, Sandra Maria Shaji, Anthony Agnesina, Guojie Luo, Sung Kyu Lim

Abstract: In this paper, we show that true 3D placement approaches, enhanced with reinforcement learning, can offer further PPA improvements over pseudo-3D approaches. To accomplish this goal, we integrate an academic true 3D placement engine into a commercial-grade 3D physical design flow, creating ART-3D flow (Analytical 3D Placement with Reinforced Parameter Tuning-based 3D flow). We use a reinforcement learning (RL) framework to find optimized placement parameter settings of the true 3D placement engine for a given netlist and perform high-quality 3D placement. We then use an efficient 3D optimization and routing engine based on a commercial place and route (P&R) tool to maintain or improve the benefits reaped from true 3D placement till design signoff. We evaluate our 3D flow by designing several gate-only and processor benchmarks on a commercial 28nm technology node. Our proposed 3D flow involving true 3D placement offers the best PPA results compared to existing 3D P&R flows and reduces power consumption by up to 31%, improves effective frequency by up to 25%, and therefore reduces power-delay product by up to 43% compared with commercial 2D IC design flow. These improvements predominantly come from RL-based parameter tuning, as it improves the performance of the 3D placer by up to 12%.