ΣnLBDR: generic congestion handling routing implementation for two‐dimensional mesh network‐on‐chip. Issue 5 (1st September 2016)

• Record Type: Journal Article
• Authors: Gupta, Niyati; Sharma, Ashish; Laxmi, Vijay; Gaur, Manoj Singh; Zwolinski, Mark; Bishnoi, Rimpy
• Is Part Of: IET computers & digital techniques. Volume 10:Issue 5(2016)
• Page Start: 226
• Page End: 232
• Publication Date: 2016-09-01
• Subjects: network‐on‐chip -- network routing; generic congestion handling routing implementation -- σnLBDR -- two‐dimensional mesh network‐on‐chip -- communication mechanisms -- on‐chip congestion -- congestion avoidance -- power reduction -- area reduction -- packet latency reduction -- baseline table‐based approach; Computers -- Periodicals; Digital electronics -- Periodicals; Computer engineering -- Periodicals; Computer architecture -- Periodicals; Computer organization -- Periodicals; 621.39
• DOI: 10.1049/iet-cdt.2015.0196
• Languages: English
• ISSNs: 1751-8601

Abstract

Abstract : The number of cores on a chip is increasing from a few cores to thousands. However, the communication mechanisms for these systems do not scale at the same pace, leading to certain challenges. One of them is on‐chip congestion. There are many table‐based approaches for congestion handling and avoidance, but these are not acceptable as they impose high area and power overheads. In this study, the authors propose two congestion handling strategies aiming to capture the congestion in few bits to avoid congested routes. The first approach called σ n LBDR (logic based distributed routing) captures congestion present at nodes n ‐hop away from the current node, reducing area, power and overall packet latency. However, all nodes in the network do not experience same congestion level. For this, their second approach, weighted σ n LBDR, uses a different set of bits for each node and results in the further improvement in area and power. This study shows a comparison of both approaches with each other and also with other similar approaches. From their experimental results, they show that σ n LBDR and weighted σ n LBDR improve latency by 20 and 30%, respectively, and have less area and power overhead as compared with baseline table‐based approach.