LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip

by   Febin Sunny, et al.

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 better energy efficiency than the best known prior work on approximate communication with silicon photonic interconnects, for the same application output quality



There are no comments yet.


page 3

page 5

page 6


ARXON: A Framework for Approximate Communication over Photonic Networks-on-Chip

The approximate computing paradigm advocates for relaxing accuracy goals...

Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators

Neural approximate computing gains enormous energy-efficiency at the cos...

Standards for Energy Efficient Virtualization, Content Distribution and Big Data in Beyond 5G Networks

Power consumption in communication networks and the supporting computing...

Do Energy-oriented Changes Hinder Maintainability?

Energy efficiency is a crucial quality requirement for mobile applicatio...

Slim NoC: A Low-Diameter On-Chip Network Topology for High Energy Efficiency and Scalability

Emerging chips with hundreds and thousands of cores require networks wit...

Architectural exploration of heterogeneous memory systems

Heterogeneous systems appear as a viable design alternative for the dark...

Zero Aware Configurable Data Encoding by Skipping Transfer for Error Resilient Applications

In this paper, we propose Zero Aware Configurable Data Encoding by Skipp...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.