Architecture decomposition in system synthesis of heterogeneous many-core systems

Determining feasible application mappings for Design Space Exploration (DSE) and run-time embedding is a challenge for modern many-core systems. The underlying NP-complete system-synthesis problem faces tremendously complex problem instances due to the hundreds of heterogeneous processing elements, their communication infrastructure, and the resulting number of mapping possibilities. Thus, we propose to employ a search-space splitting (SSS) technique using architecture decomposition to increase the performance of existing design-time and run-time synthesis approaches. The technique first restricts the search for application embeddings to selected sub-architectures at substantially reduced complexity; therefore, the complete architecture needs to be searched only in case no embedding is found on any sub-system. Furthermore, we introduce a basic learning mechanism to detect promising sub-architectures and subsequently restrict the search to those. We exemplify the SSS for a SAT-based and a problem-specific backtracking-based system synthesis as part of DSE for NoC-based many-core systems. Experimental results show drastically reduced execution times (≈ 15--50 x on a 24×24 architecture) and an enhanced quality of the embedding, since less mappings (≈20--40 x, compared to the non-decomposing procedures) need to be discarded due to a timeout.