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CLEO-CoDe: Exploiting Constrained Decoding for Cross-Layer Energy Optimization in Heterogeneous Embedded Systems

System-level design for low-power and energy efficiency in embedded systems using Heterogeneous Multi-Processor System-on-Chip (HMPSoC) is a challenging task due to the large design space. The related Design Space Exploration (DSE) suffers from scaling due to various degrees of freedom across multiple layers of the compute stack. Traditional multi-objective metaheuristic approaches work well for unconstrained system-level design, but do not scale well with the additional system and user constraints. Using a SATisfiablity problem (SAT) solver as a decoder for the meta-heuristics has been explored in related research as a better solution for a discrete constrained multi-objective optimization problem. This approach restricts the problem to the feasible space, hence improving the quality of the results. In this paper, we explore the ways in which constrained decoding such as the SAT decoding approach can be leveraged for cross-layer design space exploration. Low-power methodologies such as Dynamic Voltage and Frequncy Scaling (DVFS) and application-specific implementations are integrated, thus scaling the design space. Additionally, we demonstrate how user constraints on the system synthesis problem can be learned by our proposed approach to prune the meta-heuristic design space and improve the quality of the solutions. As the constraints on the problem increase and the design space scales, the constrained decoding approach outperforms a typical meta-heuristic approach.