Revolutionizing Deep Learning Energy Measurement with FECoM

By Ashutosh RoyReviewed by Susha Cheriyedath, M.Sc.Aug 27 2023

In a recent paper submitted to the arXiv* server, the researchers introduced a novel framework called Fine-grained Energy Consumption Meter (FECoM) and assessed its capability in addressing the challenges of fine-grained energy measurement for DL models. They also explored FECoM’s capabilities and examined the implications of its findings.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

As the adoption of Deep Learning (DL) models grows across various domains like medicine, transportation, education, and finance, concerns about the environmental impact of their energy consumption have become increasingly important. The need to promote energy efficiency in DL applications has led researchers to seek ways to measure and optimize energy consumption at granular levels. In response to this demand, a team of researchers from Dalhousie University, University College London, and Nanyang Technological University has developed a novel framework named FECoM.

Understanding the Energy Challenge

The rapid proliferation of DL models has resulted in a surge in energy consumption. Training complex models requires substantial computational resources, leading to increased carbon emissions and costs. For instance, training a single MegatronLM model can consume as much energy as powering three American households for a year. This unsustainable trend emphasizes the need for energy-aware development practices without compromising model accuracy. This is where FECoM steps in to revolutionize energy measurement and optimization.

FECoM: A Fine-Grained Solution

FECoM is a pioneering framework designed to accurately measure DL application energy consumption at the method level. Developed by a collaboration of researchers from various universities, FECoM fills a critical gap in the field by providing a mechanism to profile DL APIs with unprecedented precision. Traditional methods of energy measurement focus on system-level data, while FECoM takes a granular approach, considering factors such as computational load and temperature stability.

FECoM's architecture revolves around static instrumentation. It identifies target method calls within a program and inserts code snippets before and after these calls to capture energy consumption data. The inserted code monitors stability conditions such as temperature and energy fluctuations, ensuring reliable measurements. FECoM uses a combination of tools, including Intel's Running Average Power Limit (RAPL), NVIDIA's System Management Interface (nvidia-smi), and lm-sensors, to monitor and record power consumption, temperature, and other relevant metrics.

To assess FECoM's effectiveness, the researchers posed several research questions and conducted rigorous experiments:

FECoM's method-level energy measurement: FECoM's accuracy was evaluated by comparing the sum of the energy consumption of measured methods with the energy consumption of the entire project. The results demonstrated that FECoM's measurements were within the expected range, verifying its ability to accurately measure energy consumption at the method level.

Impact of input size on energy: By altering the input data size of DL APIs, the researchers investigated the relationship between input size and energy consumption. The findings suggested a direct proportionality, reinforcing the intuitive notion that larger inputs lead to higher energy consumption. This insight is valuable for designing energy-efficient APIs and understanding the trade-offs between accuracy and energy usage.

However, developing a framework like FECoM posed its own challenges. The researchers encountered obstacles related to stability checks, accuracy, and measuring energy at fine-grained levels. These challenges underscore the complexity of fine-grained energy measurement and the need for comprehensive tools like FECoM to address them.

Conclusion

Overall, FECoM emerges as a groundbreaking solution to address the urgent need for accurate energy measurement in Deep Learning. Its capability to measure energy consumption at the method level provides researchers and developers with a powerful tool for optimizing energy consumption without compromising model accuracy. FECoM's findings about the impact of input data size on energy consumption contribute to more energy-efficient design practices, paving the way for a greener future in AI development.

As DL continues to transform industries, FECoM's impact extends beyond the laboratory, potentially driving the development of more energy-aware DL frameworks and practices. By shining a light on the energy profile of DL APIs, FECoM empowers researchers and developers to make informed decisions, mitigate environmental impact, and work towards a sustainable AI future.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.