System-level performance analysis for designing on-chip communication architectures

  • Authors:
  • K. Lahiri;A. Raghunathan;S. Dey

  • Affiliations:
  • Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA;-;-

  • Venue:
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Year:
  • 2006

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Abstract

This paper presents a novel system-level performance analysis technique to support the design of custom communication architectures for system-on-chip integrated circuits. Our technique fills a gap in existing techniques for system-level performance analysis, which are either too slow to use in an iterative communication architecture design framework (e.g., simulation of the complete system) or are not accurate enough to drive the design of the communication architecture (e.g., techniques that perform a “static” analysis of the system performance). Our technique is based on a hybrid trace-based performance-analysis methodology in which an initial cosimulation of the system is performed with the communication described in an abstract manner (e.g., as events or abstract data transfers). An abstract set of traces are extracted from the initial cosimulation containing necessary and sufficient information about the computations and communications of the system components. The system designer then specifies a communication architecture by: 1) selecting a topology consisting of dedicated as well as shared communication channels (shared buses) interconnected by bridges; 2) mapping the abstract communications to paths in the communication architecture; and 3) customizing the protocol used for each channel. The traces extracted in the initial step are represented as a communication analysis graph (CAG) and an analysis of the CAG provides an estimate of the system performance as well as various statistics about the components and their communication. Experimental results indicate that our performance-analysis technique achieves accuracy comparable to complete system simulation (an average error of 1.88%) while being over two orders of magnitude faster