DSPlacer: DSP Placement for FPGA-based CNN Accelerator

Deploying convolutional neural networks (CNNs) on hardware platforms like Field Programmable Gate Arrays (FPGAs) has garnered significant attention due to their inherent flexibility and parallelism. Achieving optimal timing closure remains a critical challenge, as placement directly impacts clock frequency and throughput. Existing approaches often face scalability issues with large designs or fail to formalize placement rules into automated algorithms. In this paper, we propose DSPlacer, a novel DSP placement framework designed for diverse CNN accelerator architectures in the context of FPGA design. The proposed approach iteratively optimizes the placement of datapath DSPs to enhance timing performance. To achieve this, DSPlacer integrates several advanced techniques, including graph convolutional network-based datapath DSP identification, DSP graph construction, min-cost-flow DSP assignment, and integer linear programming (ILP)-based cascade constraint legalization. These techniques collectively address two key requirements for datapath DSP placement: (1) cascading datapath DSPs to achieve a compact layout, and (2) preserving direct datapath information between the processing system and programmable logic. The framework has been evaluated on multiple academic benchmarks and compared against AMD Xilinx Vivado 2020.2 and AMF-Placer 2.0. Experimental results demonstrate that DSPlacer improves Worst Negative Slack (WNS) by 32% and 65%, respectively, highlighting its efficacy and superiority.