Sparse Matrix Methods for Circuit Simulation Problems

Davis, Timothy A.; Natarajan, Ekanathan Palamadai

doi:10.1007/978-3-642-22453-9_1

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…As a base line of comparison we choose the KLU solver [23,38] within the TITAN simulator. This solver is well suited for sparse matrices that arise from analog circuit simulation, and this solver is also widely used in open-source [4,5] and commercial SPICE simulators.…”

Section: Resultsmentioning

confidence: 99%

“…This solver is well suited for sparse matrices that arise from analog circuit simulation, and this solver is also widely used in open-source [4,5] and commercial SPICE simulators. Since KLU [23,38] does not have a parallel version, we consider the elapsed time of the sequential simulation as a reference for the sequential and parallel simulations with our approach.…”

Section: Resultsmentioning

confidence: 99%

“…As it is shown in Table 2, turning on the partition bypassing in sequential mode does not have any significant effect on the number of time steps nor on the number of iterations. Therefore The base line for comparison is a simulation with the KLU solver [23,38]. The sequential run with the BBD1 solver is the presented approach but without partition bypassing.…”

Section: Resultsmentioning

confidence: 99%

“…In this publication we presented a holistic approach to achieve double digit speedups for analog transient simulation of large-sized circuits compared to existing state-of-the-art KLU [23,38] solver. The novelty of our approach consists in the combination and extension of existing approaches in a unique and unprecedented way.…”

Section: Discussionmentioning

confidence: 99%

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A holistic fast and parallel approach for accurate transient simulations of analog circuits

2017

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The accurate analog simulation of critical circuit parts is a key task in the R&D process of integrated circuits. With the increasing complexity of integrated circuits it is becoming cumulatively challenging to simulate in the analog domain and within reasonable simulation time. Previous speedup approaches of the SPICE (Simulation Program with Integrated Circuit Emphasis) analog circuit simulator included either solver improvements and speedup or model order reduction of the semiconductor devices.In this paper we present a comprehensive approach to significantly speedup a SPICE-based analog circuit simulator while keeping the single-rate characteristic of time domain simulations. The novelty of our approach consists in the combination and extension of existing approaches in a unique way, enabling fast transient SPICE-level simulations. The main component of our approach is the circuit partitioner that combines relevant aspects from circuit theory and linear algebra in a unifying way. This enables the construction of an efficient and parallel BBD (bordered block diagonal) solver. Furthermore, this BBD structure allows for intrinsic model order reduction of the partitions during the Newton iteration, transforming the Newton method to a Quasi-Newton method.For mid-sized and large-sized circuits our BBD approach leads to significant sequential and parallel accelerations of transient simulations. Additional speedup can be gained from our block-bypass strategies exploiting the latency in the partitioned circuit. Altogether our approach leads to a speedup of up to two orders of magnitude compared to the state-of-the-art KLU solver while maintaining SPICE-level accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A holistic fast and parallel approach for accurate transient simulations of analog circuits

2017

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“…The system is solved by a Newton-Raphson procedure. For efficient direct methods to solve the intermediate linear systems, see [1]. A fixed Jacobian can reduce the number of LUdecompositions, but, in general, will increase the number of iterations and thus the number of (costly) evaluations.…”

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confidence: 99%