Mapping task graphs onto Network Processors using genetic algorithm

Weng, Ning; Kumar, Niraj; Dechu, Satish; Soewito, Benfano

doi:10.1109/aiccsa.2008.4493576

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Various techniques for task mapping in conventional wired Network-on-Chip have been discussed in prior research papers [11][12][13][14][15][16][17][18][19]. Static mapping techniques for conventional wired NoC have been covered in [13] and [15] while [12] and [11] introduce two genetic mapping approaches. However, these methods are not well-suited for real-time applications that are subject to constraints and deadlines.…”

Section: Related Workmentioning

confidence: 99%

Cluster Based Adaptive Multi-Voltage Scaling Dynamic Task Mapping for WNoC and HWNoC

Ghosh,

Agarwal,

Sinha

et al. 2024

Preprint

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Wireless Network-on-Chip (WNoC) and Hybrid Wireless Network-on-Chip (HWNoC) architectures are promising solutions for future high-performance computing systems. However, WNoC consumes significant power, while HWNoC experiences congestion over the wireless link. Several state-of-the-art task mapping algorithms have been proposed to reduce power consumption and congestion over wireless links. However, these existing task mapping algorithms face challenges related to hotspots creation, sub-optimal utilization of wireless links, and also overlook idle core power reduction strategy. Additionally, each of the existing task mapping algorithms is designed for a specific architecture, either WNoC or HWNoC. To address these challenges we propose a novel task mapping algorithm called Cluster-Based Adaptive Multi-Voltage Scaling (CB-AMS). This algorithm dynamically maps tasks to clusters while performing multi-voltage scaling based on workload to significantly reduce power consumption and congestion over wireless links. A new cluster selection strategy is also proposed in CB-AMS to address the hotspot creation issue. CB-AMS is designed to be used in both WNoC and HWNoC architecture. Experimental results show that CB-AMS significantly reduces power consumption by 41% for WNoC and by 15-20% for HWNoC compared to state-of-the-art task mapping algorithms. Experimental results also validate that CB-AMS achieves better congestion control in HWNoC architecture by reducing latency by 3.6-5.5% compared to existing task mapping algorithms. Our experimental analysis has demonstrated that CB-AMS outperforms the current algorithms and delivers significant power reduction and improved congestion control for both WNoC and HWNoC architectures.

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