IoTCO2: Assessing the End-To-End Carbon Footprint of
  Internet-of-Things-Enabled Deep Learning
Ahmad Faiz Shahzeen Attari Gayle Buck Fan Chen Lei Jiang
To improve privacy and ensure quality-of-service (QoS), deep learning (DL)
models are increasingly deployed on Internet of Things (IoT) devices for data
processing, significantly increasing the carbon footprint associated with DL on
IoT, covering both operational and embodied aspects. Existing operational
energy predictors often overlook quantized DL models and emerging neural
processing units (NPUs), while embodied carbon footprint modeling tools neglect
non-computing hardware components common in IoT devices, creating a gap in
accurate carbon footprint modeling tools for IoT-enabled DL. This paper
introduces \textit{\carb}, an end-to-end modeling tool for precise carbon
footprint estimation in IoT-enabled DL, demonstrating a maximum $\pm21\%$
deviation in carbon footprint values compared to actual measurements across
various DL models. Additionally, practical applications of \carb are showcased
through multiple user case studies.