Leveraging Bayesian Optimization to Speed Up Automatic Precision Tuning

International audienceUsing just the right amount of numerical precision is an important aspect for guaranteeing performance and energy efficiency requirements. Word-Length Optimization (WLO) is the automatic process for tuning the precision, i.e., bit-width, of variables and operations represented using fixed-point arithmetic. However, state-of-the-art precision tuning approaches do not scale well in large applications where many variables are involved. In this paper, we propose a hybrid algorithm combining Bayesian optimization (BO) and a fast local search to speed up the WLO procedure. Through experiments, we first show some evidence on how this combination can improve exploration time. Then, we propose an algorithm to automatically determine a reasonable transition point between the two algorithms. By statistically analyzing the convergence of the probabilistic models constructed during BO, we derive a stopping condition that determines when to switch to the local search phase. Experimental results indicate that our algorithm can reduce exploration time by up to 50%-80% for large benchmarks

Cracking The Complexity Of Fixed-Point Refinement In Complex Wireless Systems

Fixed-point arithmetic leads to efficient implementations. However, the optimization process required to size each of the implementation signals can be prohibitively complex. In this paper, we introduce a new divide-and-conquer method that is able to approach the quality of global methods in significantly less time. Firstly, our method sorts the signals in multiple groups considering the propagation path of the signals to the global application metric (i.e., bit-error rate). Then, the fixed-point configurations of the groups are resolved with fast local simulations. Finally, the global fixed-point configuration is composed by the group configurations using slow global simulations. The method is applied to the fixed-point refinement of an advanced wireless algorithm achieving close to 9 times speedup with respect to a reference statistical method without affecting the quality of the result