Emulation-Based Analysis of System-on-Chip Performance Under Variations

The scaling of integrated circuits into the nanometer regime has led to variations emerging as a primary design concern. Most efforts in the area of variation-tolerant design have focused on the physical, circuit, and logic levels of abstraction. However, inevitable increases in the magnitude of variations with scaling have elevated them to a design concern that must be addressed starting at the system level. We address the problem of analyzing the performance of system-onchip (SoC) architectures in the presence of variations. A modern SoC is a complex ensemble of components that are organized into multiple voltage and frequency domains or islands. The impact of variations on the clock frequencies of individual SoC components may be analyzed using existing tools, such as circuit-level statistical timing analysis. However, the key challenge that needs to be addressed is how to translate these component-level clock frequency distributions into a system-level performance distribution. This task is particularly complex and challenging due to the interdependences between components' execution, indirect effects of shared resources, and interactions between multiple system-level execution paths. We argue that an accurate variation-aware performance analysis requires Monte Carlo-based repeated system execution. We describe a framework variability emulation for SoC performance analysis (VESPA)-that leverages emulation to significantly speed up the performance analysis without sacrificing the generality and accuracy achieved by Monte Carlo-based simulation. We further improve the efficiency of VESPA by utilizing correlated sampling to reduce the number of samples needed for Monte Carlo simulations. We demonstrate the utility of VESPA by applying it to design variation-tolerant architectures for three example SoCs. Our experiments show the performance improvements of ~180× compared with the state-of-the-art hardware-software cosimulation tools and also underscore the potential of VESPA to enable variation-aware design and exploration at the system level.