MOGAC: a multiobjective genetic algorithm for hardware-software cosynthesis of distributed embedded systems

In this paper, we present a hardware-software cosynthesis system, called MOGAC, that partitions and schedules embedded system specifications consisting of multiple periodic task graphs. MOGAC synthesizes real-time heterogeneous distributed architectures using an adaptive multiobjective genetic algorithm that can escape local minima. Price and power consumption are optimized while hard real-time constraints are met. MOGAC places no limit on the number of hardware or software processing elements in the architectures it synthesizes. Our general model for bus and point-to-point communication links allows a number of link types to be used in an architecture. Application-specific integrated circuits consisting of multiple processing elements are modeled. Heuristics are used to tackle multi-rate systems, as well as systems containing task graphs whose hyperperiods are large relative to their periods. The application of a multiobjective optimization strategy allows a single cosynthesis run to produce multiple designs which trade off different architectural features. Experimental results indicate that MOGAC has advantages over previous work in terms of solution quality and running time. Keywords— Genetic algorithm, hardware-software co-synthesis, low-power synthesis, multiobjective optimization