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

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.