Compilation of Dataflow Applications for Multi-Cores using Adaptive Multi-Objective Optimization

Abstract

State-of-the-art system synthesis techniques employ meta-heuristic optimization techniques for Design Space Exploration (DSE) to tailor application execution, e.g., defined by a dataflow graph, for a given target platform. Unfortunately, the performance evaluation of each implementation candidate is computationally very expensive, in particular on recent multi-core platforms, as this involves compilation to and extensive evaluation on the target hardware. Applying heuristics for performance evaluation on the one hand allows for a reduction of the exploration time but on the other hand may deteriorate the convergence of the optimization technique toward performance-optimal solutions with respect to the target platform. To address this problem, we propose DSE strategies that are able to dynamically trade off between (i) approximating heuristics to guide the exploration and (ii) accurate performance evaluation, i.e., compilation of the application and subsequent performance measurement on the target platform. Technically, this is achieved by introducing a set of additional, but easily computable guiding objective functions, and varying the set of objective functions that are evaluated during the DSE adaptively. One major advantage of these guiding objectives is that they are generically applicable for dataflow models without having to apply any configuration techniques to tailor their parameters to the specific use case. We show this for synthetic benchmarks as well as a real-world control application. Moreover, the experimental results demonstrate that our proposed adaptive DSE strategies clearly outperform a state-of-the-art DSE approach known from literature in terms of the quality of the gained implementations as well as exploration times. Amongst others, we show a case for a two-core implementation where after about 3 hours of exploration time one of our proposed adaptive DSE strategies already obtains a 60% higher performance value than obtained by the state-of-the-art approach. Even when the state-of-the-art approach is given a total exploration time of more than 2 weeks to optimize this value, the proposed adaptive DSE strategy features a 20% higher performance value after a total exploration time of about 4 days.