Photonics for Sustainable Computing
Farbin Fayza Satyavolu Papa Rao Darius Bunandar Udit Gupta Ajay Joshi
Photonic integrated circuits are finding use in a variety of applications
including optical transceivers, LIDAR, bio-sensing, photonic quantum computing,
and Machine Learning (ML). In particular, with the exponentially increasing
sizes of ML models, photonics-based accelerators are getting special attention
as a sustainable solution because they can perform ML inferences with multiple
orders of magnitude higher energy efficiency than CMOS-based accelerators.
However, recent studies have shown that hardware manufacturing and
infrastructure contribute significantly to the carbon footprint of computing
devices, even surpassing the emissions generated during their use. For example,
the manufacturing process accounts for 74% of the total carbon emissions from
Apple in 2019. This prompts us to ask -- if we consider both the embodied
(manufacturing) and operational carbon cost of photonics, is it indeed a viable
avenue for a sustainable future? So, in this paper, we build a carbon footprint
model for photonic chips and investigate the sustainability of photonics-based
accelerators by conducting a case study on ADEPT, a photonics-based accelerator
for deep neural network inference. Our analysis shows that photonics can reduce
both operational and embodied carbon footprints with its high energy efficiency
and at least 4$\times$ less fabrication carbon cost per unit area than 28 nm
CMOS.