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

Lengfei Han, Xueqian Zhao, Zhuo Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

In nowadays variation-aware IC designs, cell characterizations and SRAMmemory 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.

Original languageEnglish
Title of host publicationProceedings of the 50th Annual Design Automation Conference, DAC 2013
DOIs
StatePublished - 2013
Event50th Annual Design Automation Conference, DAC 2013 - Austin, TX, United States
Duration: 29 May 20137 Jun 2013

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X

Conference

Conference50th Annual Design Automation Conference, DAC 2013
Country/TerritoryUnited States
CityAustin, TX
Period29/05/137/06/13

Keywords

  • GPU computing
  • SPICE simulation
  • Variation-aware analysis

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