TinySPICE: A parallel SPICE simulator on GPU for massively repeated small circuit simulations

Lengfei Han, Xueqian Zhao, Zhuo Feng
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引用次数: 19

Abstract

In nowadays variation-aware IC designs, cell characterizations and SRAM memory yield analysis require many thousands or even millions of repeated SPICE simulations for relatively small nonlinear circuits. In this work, we present a massively parallel SPICE simulator on GPU, TinySPICE, for efficiently analyzing small nonlinear circuits, such as standard cell designs, SRAMs, etc. In order to gain high accuracy and efficiency, we present GPU-based parametric three-dimensional (3D) LUTs for fast device evaluations. A series of GPU-friendly data structures and algorithm flows have been proposed in TinySPICE to fully utilize the GPU hardware resources, and minimize data communications between the GPU and CPU. Our GPU implementation allows for a large number of small circuit simulations in GPU's shared memory that involves novel circuit linearization and matrix solution techniques, and eliminates most of the GPU device memory accesses during the Newton-Raphson (NR) iterations, which enables extremely high-throughput SPICE simulations on GPU. Compared with CPU-based TinySPICE simulator, GPU-based TinySPICE achieves up to 138X speedups for parametric SRAM yield analysis without loss of accuracy.
TinySPICE: GPU上的并行SPICE模拟器,用于大规模重复的小电路模拟
在当今的变化感知IC设计中,单元表征和SRAM存储器产率分析需要成千上万甚至数百万个相对较小的非线性电路的重复SPICE模拟。在这项工作中,我们提出了一个基于GPU的大规模并行SPICE模拟器TinySPICE,用于有效分析小型非线性电路,如标准单元设计,sram等。为了获得更高的精度和效率,我们提出了基于gpu的参数化三维(3D) LUTs,用于快速器件评估。在TinySPICE中提出了一系列GPU友好的数据结构和算法流程,以充分利用GPU硬件资源,最大限度地减少GPU与CPU之间的数据通信。我们的GPU实现允许在GPU的共享内存中进行大量小电路模拟,涉及新颖的电路线性化和矩阵求解技术,并在牛顿-拉夫森(NR)迭代期间消除了大多数GPU设备内存访问,从而实现GPU上的极高吞吐量SPICE模拟。与基于cpu的TinySPICE模拟器相比,基于gpu的TinySPICE在不损失精度的情况下实现了参数SRAM良率分析的高达138X的速度。
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