Parallel Acceleration for Timing Analysis and Optimization of Adaptive Integrated Circuits
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Adaptive circuit design is a power-efficient approach to handle variations. Compared to conventional circuits, its implementation is more complicated especially when we deal with the fine-grained adaptivity. The unconventional and sophisticated nature of adaptive design further requires timing verification to validate the design. However timing analysis becomes more complicated due to complexities arising from nanometer VLSI technologies. A well-known challenge is process variations, which need to be addressed in timing analysis at least by considering different process corners. Adaptive circuit design further needs statistical static timing analysis (SSTA), which is much more time consuming than variation-oblivious timing analysis. Besides timing analysis, gate implementation selections of the adaptive design process are also computational expensive. This research focuses on parallel acceleration techniques for timing analysis and optimization of adaptive circuit. General purpose graphic processing units (GPGPU) and multithreading techniques are used in this work. Previous works on GPU acceleration for SSTA are mostly based on Monte Carlo based SSTA. By contrast, the parallelization techniques for principle component analysis (PCA) based SSTA are explored in this work, which is intrinsically more efficient. We develop a batch-based scheduling algorithm to partition the circuit graph into topological levels for GPU processing and investigate other techniques such as latency hiding. We propose a multithreading based acceleration method for the process of gate implementation selection and use the same batch-based scheduling result. The experiment result shows effectiveness of our parallel acceleration for timing analysis and for optimization with the performance up to 130× and 5× speedup respectively.
Shen, Yiren (2016). Parallel Acceleration for Timing Analysis and Optimization of Adaptive Integrated Circuits. Master's thesis, Texas A & M University. Available electronically from