Core Concepts
Generative AI holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) through its excellent reasoning and generation capabilities.
Abstract
The article explores the potential of Generative AI (GAI) for carbon emissions reduction and proposes a novel GAI-enabled solution for low-carbon AIoT.
Key highlights:
The main impacts that cause carbon emissions in AIoT are investigated, including the challenges of energy consumption and carbon emissions due to the continuous advancement of mobile technology.
GAI techniques and their relations to carbon emissions are introduced, including Generative Adversarial Networks (GANs), Retrieval Augmented Generation (RAG), and Generative Diffusion Models (GDMs).
The application prospects of GAI in low-carbon AIoT are explored, focusing on how GAI can reduce carbon emissions of network components such as the Energy Internet, data center networks, and mobile edge networks.
An LLM-enabled carbon emission optimization framework enabled by RAG is proposed, where LLMs are used to generate accurate and reliable optimization problems, and GDMs are utilized to identify optimal strategies for carbon emission reduction.
A case study on mobile Artificial Intelligence-Generated Content (AIGC) task offloading in a metaverse environment is conducted, demonstrating the effectiveness of the proposed framework.
Open research directions for low-carbon AIoT are discussed, including carbon emission minimization problems for cloud-edge-device architectures, GAI-enabled carbon trading, training optimization for GAI models, and carbon-aware deployment of GAI models.
Stats
The global deployment of edge devices is projected to rise from 2.7 billion to 7.8 billion in the next decade.
The broader category of IoT-connected devices is expected to surpass 30 billion worldwide by 2025.
The mobile data traffic of a mobile device will reach 257.1 GB per month by 2030, a 50-fold increase compared to 2010.
China's 5G network generates more than 60 million tons of carbon emissions nationwide every year.
The satellite fleet causes 37,484 tons of carbon emissions every year.
There were 7.7 billion mobile phones in use worldwide in 2020, producing about 580 million tons of carbon emissions, equivalent to about 1% of total global emissions.
The energy consumption for training a ResNet-110 model on the NVIDIA Jetson TX2 platform amounts to approximately 8 × 105 Joules of energy.
Quotes
"The rapid growth in power consumption of edge loads and the scarcity of energy resources pose significant energy challenges to AIoT, resulting in new environmental impacts."
"GAI opens up new avenues to achieve low-carbon AIoT."
"Unlike traditional green mobile networks, which primarily focus on reducing energy consumption and enhancing energy efficiency, low-carbon AIoT enabled by GAI focuses on utilizing GAI to minimize carbon emissions and promotes sustainable practices across the entire network ecosystem."