Quantitative analysis of the speed/accuracy trade-off in transaction level modeling

Abstract

The increasing complexity of embedded systems requires modeling at higher levels of abstraction. Transaction level modeling (TLM) has been proposed to abstract communication for high-speed system simulation and rapid design space exploration. Although being widely accepted for its high performance and efficiency, TLM often exhibits a significant loss in model accuracy. In this article, we systematically analyze and quantify the speed/accuracy trade-off in TLM. To this end, we provide a classification of TLM abstraction levels based on model granularity and define appropriate metrics and test setups to quantitatively measure and compare the performance and accuracy of such models. Addressing several classes of embedded communication protocols, we apply our analysis to three common bus architectures, the industry-standard AMBA advanced high-performance bus (AHB) as an on-chip parallel bus, the controller area network (CAN) as an off-chip serial bus, and the Motorola ColdFire Master Bus as an example for a custom embedded processor bus. Based on the analysis of these individual busses, we then generalize our results for a broader conclusion. The general TLM trade-off offers gains of up to four orders of magnitude in simulation speed, generally however, at the price of low accuracy. We conclude further that model granularity is the key to efficient TLM abstraction, and we identify conditions for accuracy of abstract models. As a result, this article provides general guidelines that allow the system designer to navigate the TLM trade-off effectively and choose the most suitable model for the given application with fast and accurate results.