论文标题
GATSPI：GPU加速登机口级模拟，以提高功率
GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement
论文作者
论文摘要
在本文中，我们提出了GATSPI，这是一种新型的GPU加速逻辑门模拟器，可实现对行业大小的ASIC设计的超快速功率估计，并具有数百万个大门。 Gatspi用Pytorch编写，并带有自定义CUDA内核，以易于编码和可维护性。与在单个CPU核心上运行的商用门级模拟器相比，在单个GPU系统上，在单个GPU系统上达到了高达1668X的模拟内核加速度，在多GPU系统上达到7412X。 GATSPI支持来自行业标准单元格库和SDF条件延迟语句的一系列简单至复杂的单元类型，而无需先前的校准运行，并从延迟吸引的门级仿真中生成行业标准的SAIF文件。最后，我们将GATSPI部署在小故障流动流中，与使用商业模拟器的类似流量相比，在周转时间内，可以通过449倍加速实现1.4％的功率。
In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates. GATSPI is written in PyTorch with custom CUDA kernels for ease of coding and maintainability. It achieves simulation kernel speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU system when compared to a commercial gate-level simulator running on a single CPU core. GATSPI supports a range of simple to complex cell types from an industry standard cell library and SDF conditional delay statements without requiring prior calibration runs and produces industry-standard SAIF files from delay-aware gate-level simulation. Finally, we deploy GATSPI in a glitch-optimization flow, achieving a 1.4% power saving with a 449X speedup in turnaround time compared to a similar flow using a commercial simulator.
