Towards Generic and Scalable Word-Length Optimization
Van-Phu Ha1,a, Tomofumi Yuki2 and Olivier Sentieys1,b
1Univ Rennes, Inria, IRISA Rennes, France
avan-phu.ha@inria.fr
bolivier.sentieys@inria.fr
2Inria, Univ Rennes, IRISA Rennes, France
tomofumi.yuki@inria.fr
ABSTRACT
In this paper, we propose a method to improve the scalability of Word-Length Optimization (WLO) for large applications that use complex quality metrics such as Structural Similarity (SSIM). The input application is decomposed into smaller kernels to avoid uncontrolled explosion of the exploration time, which is known as noise budgeting. The main challenge addressed in this paper is how to allocate noise budgets to each kernel. This requires capturing the interactions across kernels. The main idea is to characterize the impact of approximating each kernel on accuracy/cost through simulation and regression. Our approach improves the scalability while finding better solutions for Image Signal Processor pipeline.
Keywords: Fixed-Point Refinement, Word-length Optimization, Noise Budgeting, Multiple Kernel Optimization
