From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus
Dian-Lun Lin, Haoxing Ren, Yanqing Zhang, Brucek Khailany, Tsung‐Wei Huang
Abstract
High-throughput RTL simulation is critical for verifying today’s highly complex SoCs. Recent research has explored accelerating RTL simulation by leveraging event-driven approaches or partitioning heuristics to speed up simulation on a single stimulus. To further accelerate throughput performance, industry-quality functional verification signoff must explore running multiple stimulus (i.e., batch stimulus) simultaneously, either with directed tests or random inputs. In this paper, we propose RTLFlow, a GPU-accelerated RTL simulation flow with batch stimulus. RTLflow first transpiles RTL into CUDA kernels that each simulates a partition of the RTL simultaneously across multiple stimulus. It also leverages CUDA Graph and pipeline scheduling for efficient runtime execution. Measuring experimental results on a large industrial design (NVDLA) with 65536 stimulus, we show that RTLflow running on a single A6000 GPU can achieve a 40 × runtime speed-up when compared to an 80-thread multi-core CPU baseline.