LoraX: Loss-aware approximations for energy-efficient silicon photonic networks-on-chip

Febin Sunny, Asif Mirza, Ishan Thakkar, Sudeep Pasricha, Mahdi Nikdast

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to improve the energy efficiency of silicon photonic networks-on-chip (PNoCs). In this paper, we propose a novel framework (LORAX) to enable more aggressive approximation during communication over silicon photonic links in PNoCs. This is the first work that considers loss-aware laser power management and multilevel signaling to enable effective data approximation and energy-efficiency in PNoCs. Simulation results show that our framework can achieve up to 31.4% lower laser power consumption and up to 12.2% better energy efficiency than the best known prior work on approximate communication in PNoCs, for the same application output quality.

Original languageEnglish
Title of host publicationGLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
Number of pages6
ISBN (Electronic)9781450379441
StatePublished - Sep 7 2020
Event30th Great Lakes Symposium on VLSI, GLSVLSI 2020 - Virtual, Online, China
Duration: Sep 7 2020Sep 9 2020

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference30th Great Lakes Symposium on VLSI, GLSVLSI 2020
CityVirtual, Online

Bibliographical note

Funding Information:
This research was supported by the National Science Foundation (NSF) under grant numbers CCF-1813370 and CCF-2006788.

Publisher Copyright:
© 2020 Association for Computing Machinery.


  • Approximate computing
  • Energy efficiency
  • PNoCs

ASJC Scopus subject areas

  • Engineering (all)


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