Low power hardware-software partitioning algorithm for heterogeneous distributed embedded systems

Abstract

Hardware-software partitioning is one of the most crucial steps in the design of embedded systems, which is the process of partitioning an embedded system specification into hardware and software modules to meet performance and cost goals. A majority of former work focuses on the problem of meeting timing constraints under minimizing the amount of hardware or minimizing time under hardware area constraints. The trends towards energy-efficient design of distributed embedded systems indicate the need for low power hardware-software partitioning algorithms, which are not enough emphasized so far. In this paper, we design tabu search on a chaotic neural network to solve the low power hardware-software partitioning problem. By introducing chaotic dynamics and utilizing the refractory effects of neurons as the tabu effects, the realized tabu search gets partitioning result with lower energy consumption, when compared with genetic algorithm. © IFIP International Federation for Information Processing 2006.