Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 63 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 19 tok/s Pro
GPT-4o 100 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 472 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip (2002.11289v1)

Published 26 Feb 2020 in cs.AR

Abstract: 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. Given that silicon photonic interconnects have significant power dissipation due to the laser sources that generate the wavelengths for photonic communication, our framework attempts to reduce laser power overheads while intelligently approximating communication such that application output quality is not distorted beyond an acceptable limit. To the best of our knowledge, 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 with silicon photonic interconnects, for the same application output quality

Citations (9)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.