Many applications exhibit error forgiving features. For these applications, approximate computing provides the opportunity of accelerating the execution time or reducing power consumption, by mitigating computation effort to get an approximate result. Among the components on a chip, network-on-chip (NoC) contributes a large portion to system power and performance. In this paper, we exploit the opportunity of aggressively reducing network congestion and latency by selectively dropping data. Essentially, the importance of the dropped data is measured based on a quality model. An optimization problem is formulated to minimize the network congestion with constraint of the result quality. A lightweight online algorithm is proposed to solve this problem. Experiments show that on average, our proposed method can reduce the execution time by as much as 12.87% and energy consumption by 12.42% under strict quality requirement, speed up execution by 19.59% and reduce energy consumption by 21.20% under relaxed requirement, compared to a recent work on approximate computing approach for NoCs.

ACDC: An Accuracy- and Congestion-aware Dynamic Traffic Control Method for Networks-on-Chip

Palesi M.;
2019

Abstract

Many applications exhibit error forgiving features. For these applications, approximate computing provides the opportunity of accelerating the execution time or reducing power consumption, by mitigating computation effort to get an approximate result. Among the components on a chip, network-on-chip (NoC) contributes a large portion to system power and performance. In this paper, we exploit the opportunity of aggressively reducing network congestion and latency by selectively dropping data. Essentially, the importance of the dropped data is measured based on a quality model. An optimization problem is formulated to minimize the network congestion with constraint of the result quality. A lightweight online algorithm is proposed to solve this problem. Experiments show that on average, our proposed method can reduce the execution time by as much as 12.87% and energy consumption by 12.42% under strict quality requirement, speed up execution by 19.59% and reduce energy consumption by 21.20% under relaxed requirement, compared to a recent work on approximate computing approach for NoCs.
978-3-9819263-2-3
approximate computing; many-core system; on-chip network
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/371947
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 6
social impact