Statistical analysis of asynchronous pipelines in presence of process variation using formal models

• Develop Generalized Stochastic Petri Net models for various asynchronous pipelines.
• Statistical analysis of delay and power consumption of asynchronous pipelines.
• Distribution fitting for all of the internal delays of asynchronous pipelines.
• Validate delay and power distribution with Monte Carlo Hspice simulation.
• Reasonable accuracy in average (below 5%) and variance with distribution fitting.

Globally Asynchronous Locally Synchronous Network on Chip (GALS NoC) is one of the possible interconnect platforms in multiprocessor systems on a chip. Designing proper links and buffers in these circuits can improve their performance. An asynchronous pipeline is a key element in buffer designs. The type of pipeline and its size can influence the performance metrics such as power consumption and delay. However, asynchronous pipelines face some challenges such as performance evaluation, verification, and process variation. We consider a new formal model to overcome these challenges simultaneously. In this paper, a new statistical model for asynchronous pipelines based on Generalized Stochastic Petri Net (GSPN) has been developed. This model can be applied to different pipeline stages, in order to compare them based on the statistical analysis of performance metrics (power consumption and delay), and to analyze their performance and timing verification in presence of variation. We have explored various kinds of asynchronous pipelines, and their corresponding results show this model has reasonable accuracy in average (below 5%) and in variance, compared to the low level Monte Carlo Hspice simulation.