DRACO: A Hardware-Efficient Robot Rigid Body Dynamics Accelerator with Precision-Aware Quantization Framework

Rigid Body Dynamics (RBD) computation is a critical component of robotic control, often dominating system runtime due to its algorithmic complexity and high parallelism demands. CPUs suffer from limited parallelism and cache-unfriendly access patterns, while GPUs incur prohibitive memory-access latency and per-task response time, making them unsuitable for real-time control. Both platforms also consume excessive power for edge deployment. FPGAs offer superior latency, energy efficiency, and customizable hardware-level parallelism, emerging as promising targets for RBD acceleration. However, existing FPGA designs still face critical limitations. First, the intensive use of multiply-accumulate operations leads to high Digital Signal Processing (DSP) slices consumptionespecially for high degrees-of-freedom (DOF) robots-resulting in limited scalability. Second, RBD functions include mass matrix inversion function, which is inefficient on FPGA due to reciprocal operations falling on the longest latency path, severely limiting performance. Third, mismatched processing rates across modules introduce idle cycles, resulting in poor DSP utilization. To address these issues, we propose DRACO, a hardwareefficient and high-performance RBD accelerator based on FPGA, introducing three key innovations. First, we propose a precisionaware quantization framework that reduces DSP demand by up to$4 \times$while preserving motion accuracy. This is also the first study to systematically evaluate quantization impact on robot control and motion for hardware acceleration. Second, we leverage a hardware-efficient division deferring optimization in mass matrix inversion algorithm, which decouples reciprocal operations from the longest latency path to improve the performance. Finally, we present an inter-module DSP reuse methodology to improve DSP utilization and save DSP usage. Experiment results show that DRACO achieves up to$8 \times$throughput improvement and$7.4 \times$latency reduction over state-of-the-art (SOTA) RBD accelerators across various robot types, demonstrating its effectiveness and scalability for high-DOF robotic systems.