Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  apply multi-objective evolutionary computation to the synthesis of real-time, embedded, heterogeneous, multiprocessor systems (briefly, Multiprocessor Systems-on-Chip or MP-SoCs). Our approach simultaneously explores the architecture, the mapping and the scheduling of the system, by using multi-objective evolution. In particular,  considered three approaches: a multi-objective genetic algorithm, multi-objective Simulated Annealing, and multi-objective Tabu Search. The algorithms search for optimal architectures, in terms of processing elements (processors and hardware accelerators) and communication infrastructure, and for the best mappings and schedules of multi-rate real-time applications given objectives such as: system area, hard and soft dead-lines violations, dimensions of memory buffers.  formalize the problem, describe our flow and compare the three algorithms, dis- cussing which one performs better with respect to different classes of applications.
['Marco Ceriani', 'Fabrizio Ferrandi', 'Pier Luca Lanzi', 'Donatella Sciuto', 'Antonino Tumeo']
I am writing a research paper on a idea Multiprocessor systems-on-chip synthesis using multi-objective evolutionary computation
[{"from": "human", "value": "I am writing a research paper on a idea Multiprocessor systems-on-chip synthesis using multi-objective evolutionary computation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  apply multi-objective evolutionary computation to the synthesis of real-time, embedded, heterogeneous, multiprocessor systems (briefly, Multiprocessor Systems-on-Chip or MP-SoCs). Our approach simultaneously explores the architecture, the mapping and the scheduling of the system, by using multi-objective evolution. In particular,  considered three approaches: a multi-objective genetic algorithm, multi-objective Simulated Annealing, and multi-objective Tabu Search. The algorithms search for optimal architectures, in terms of processing elements (processors and hardware accelerators) and communication infrastructure, and for the best mappings and schedules of multi-rate real-time applications given objectives such as: system area, hard and soft dead-lines violations, dimensions of memory buffers.  formalize the problem, describe our flow and compare the three algorithms, dis- cussing which one performs better with respect to different classes of applications."}]