Unified multi‐objective mapping for network‐on‐chip using genetic‐based hyper‐heuristic algorithms

Abstract

In this study, a flexible energy-and delay-aware mapping approach is proposed for the co-optimisation of energy consumption and communication latency for network-on-chips (NoCs). A novel genetic-based hyper-heuristic algorithm (GHA) is proposed as the core algorithm. This algorithm consists of bottom-level optimisation which includes a variety of operators and top-level optimisation which selects suitable operators through a 'reward' mechanism. As this algorithm can select suitable operators automatically during the mapping process, it noticeably improves convergence speed and demonstrates excellent stability. Compared to the random algorithm, GHA can achieve on average 23.28% delay reduction and 11.81% power reduction. Compared to state-of-the-art mapping algorithms, GHA produces improved mapping results with less time, especially when the size of NoC is large.

Network‐on‐chip heuristic mapping algorithm based on isomorphism elimination for NoC optimisation

“…8 d , is an application‐specific communication, which is often used to test the efficiency of NoC design [24]. GAs are referred to [14, 18].…”

“…So, the population with isomorphism converges slowly and may not converge to one mapping sequence. The crossover way is referred to [14].…”

“…Different heuristic algorithms have been proposed in the literature which try to get the best answer in the limited computing resources, such as BMAP (batch Markovian arrival process), SMAP (smart mobile access point), NMAP (near‐optimal mapping), SA (simulated annealing algorithm), PSO (particle swarm optimisation), GA (genetic algorithm), ACO (ant colony optimisation), GHA (genetic‐based hyper‐heuristic algorithm), [7–15]. GA is a very efficient algorithm which is often used in NoC mapping solutions, [14, 16–18]. The authors of [16, 17] proposed to change the coding style of the chromosome.…”

“…The authors of [16, 17] proposed to change the coding style of the chromosome. The authors of [14, 18] proposed to change the way of crossover. All of them are aimed to solve the genetic inheritance of the mapping solutions which are based on GA.…”

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

“…Yao et al [57] proposed a MOHH framework for walking route planning in a smart city and a reinforcement learning mechanism was established to select the LLH. Xu et al [58] used a genetic-based MOHH to tackle multiobjective mapping for network-on-chip. In their MOHH, HLH selects suitable operators through a 'reward' mechanism.…”