🤖 AI Summary
This work addresses the limitations of existing FPGA placement tools, which rely on two-dimensional frameworks and struggle to effectively optimize the inter-layer timing and routing characteristics unique to 3D FPGAs. The paper presents the first complete placement flow specifically designed for 3D FPGAs, integrating partition-based initialization, adaptive cost scheduling, fine-grained delay modeling, and a 3D-aware simulated annealing move strategy to jointly optimize layer assignment and timing. Experimental results across four representative 3D architectures demonstrate that the proposed method reduces critical path delay by 2%–6% on average (up to 18%) and decreases total wirelength by 1%–5% on average (up to 10%), significantly improving both timing and routing quality.
📝 Abstract
3D field-programmable gate arrays (FPGAs) promise higher performance through vertical integration. However, existing placement tools, largely inherited from 2D frameworks, fail to capture the unique delay characteristics and optimization dynamics of 3D fabrics. We introduce a 3D FPGA placement flow that integrates partitioning-based initialization, adaptive cost scheduling, refined delay estimation, and a simulated annealing move set -- all targeted at 3D FPGA architecture. Together, these enhancements improve timing estimates and the exploration of layer assignments during placement. Compared to Verilog-To-Routing (VTR), our experiments show geometric-mean (max) critical-path delay reductions of ~3% (~7%), ~2% (~4%), ~3% (~8%), and ~6% (~18%) for four 3D architectures: 3D CB, 3D CB-O, 3D CB-I, and 3D SB, respectively. We also achieve geometric-mean (max) routed wirelength reductions of ~1% (~3%), ~2% (~8%), < 1% (~5%), and ~5% (~10%), respectively. Our work will be permissively open-sourced on GitHub.