New Framework Promises a Greener Distributed Artificial Intelligence
With a great number of AI research studies focusing on improving the accuracy of AI algorithms, far less attention has been paid to the energy costs involved.
A collaboration between researchers at Virginia Tech, Ericsson, and the AI Cross-Center Center Unit at the Technology Innovation Institute (TII) in the United Arab Emirates recently explored ways to strike the right balance between accuracy, energy usage, and precision for distributed AI.
The team specifically focused on how quantization, a technique which creates a more efficient representation of the AI state, impacts the balance between accuracy, energy usage, and convergence time within a distributed AI system.
The framework devised has potential to also be applied across other kinds of improvements.
The research won the best paper award in the Green Communication Systems and Networks Symposium category at IEEE International Conference on Communications (ICC) 2022 conference against thousands of entries.
Dr. Walid Saad, Professor in the Electrical and Computer Engineering department at Virginia Tech, who worked on the project said: “Down the road, this research promises to improve the development of more energy efficient AI applications for home automation, autonomous robotics, and unmanned drones.”
By focusing on the energy tradeoffs for implementing federated machine learning techniques, the research offers an outlook on a promising new AI approach which allows multiple devices to collaborate and improve the algorithms for each other, while still preserving privacy.
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The technique was first pioneered by Google in 2017, which is being explored to allow cars, autonomous drones, and smartphones models to be trained by sending a representation of the AI model rather than the raw data itself.
However, these applications can incur tremendous energy overheads as they scale across a more extensive network of devices. “We wanted to understand how we could design green distributed AI without sacrificing accuracy,” explained Dr. Saad.
There are several approaches to designing more efficient methods of implementing AI algorithms at scale, including quantization, sparsification, and knowledge distillation, explained Minsu Kim, Graduate Research Assistant at the Electrical and Computer Engineering department at Virginia Tech, who also contributed to the research.
With quantization, data scientists consider ways to use fewer bits to represent the state of AI models.
Sparsification uses more efficient ways to represent the changes in the current state of neural networks.
Knowledge distillation deploys larger computers to compress the best representations for other nodes.
“These three approaches are the most fundamental techniques we are seeing to reduce energy consumption for neural networks right now,” Kim explained.
Ultimately, the group decided to focus on the tradeoffs within different quantization approaches that show the most promise for improving energy efficiency for distributed AI running over wireless systems, such as 5G, in the short run.
Quantization reduces the data size for describing machine learning model updates, which requires less energy to process and transmit. However, this can also impact the accuracy of models and the amount of time it takes a neural network to converge on an optimal solution.
The research is set to help improve the energy usage required to run AI-powered apps on smartphones, cars, and autonomous drones, in which Dr. Saad added, “This could help extend the life of a smartphone or autonomous car, so they don’t consume too much energy.”
This brings up the need for researchers to consider all aspects of new AI architecture design with ‘wireless’ factored into the equation. Researchers must consider how each design aspect may reduce accuracy to acceptable levels, increasing overall energy efficiency.
Ultimately, it may take another 5-10 years for these ideas to be adopted for more safety-critical use cases like self-driving cars.
Dr. Saad aconcluded: “This is a first step towards understanding the tradeoffs and how to reduce the architecture of federated learning.
"This is a crucial step for how a researcher might examine the green impact of AI in distributed learning over wireless systems, and how that interplays with accuracy and convergence.”
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