Quantifying the Carbon Emissions of Machine Learning

Lacoste, Alexandre; Luccioni, Alexandra; Schmidt, Víctor; Dandres, Thomas

doi:10.48550/arxiv.1910.09700

Cited by 127 publications

(142 citation statements)

References 14 publications

Supporting

Mentioning

134

Contrasting

Unclassified

Order By: Relevance

“…Tab. I summarizes the energy cost and carbon footprint of each approach, computed according to [21] for private servers running in northern Italy and supplied by the national energy provider Enel, which has a carbon efficiency of 0.243 kg/kWh according to electricitymap.org. Consistently with Fig.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost

Malandrino¹,

Chiasserini²

2022

Preprint

View full text Add to dashboard Cite

Traditionally, distributed machine learning takes the guise of (i) different nodes training the same model (as in federated learning), or (ii) one model being split among multiple nodes (as in distributed stochastic gradient descent). In this work, we highlight how fog-and IoT-based scenarios often require combining both approaches, and we present a framework for flexible parallel learning (FPL), achieving both data and model parallelism. Further, we investigate how different ways of distributing and parallelizing learning tasks across the participating nodes result in different computation, communication, and energy costs. Our experiments, carried out using state-of-the-art deep-network architectures and large-scale datasets, confirm that FPL allows for an excellent trade-off among computational (hence energy) cost, communication overhead, and learning performance.

show abstract

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

“…CONSUMPTION AND CARBON FOOTPRINT ASSOCIATED WITH FPL AND ITS ALTERNATIVES. FIGURES ARE COMPUTED ACCORDING TO[21] …”

mentioning

confidence: 99%

Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost

Malandrino¹,

Chiasserini²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Estimations were conducted using the MachineLearning Impact calculator presented in Lacoste et al (2019).…”

Section: Ethics Statementmentioning

confidence: 99%

Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation

Mai¹,

Mani²,

Paull³

2022

Preprint

View full text Add to dashboard Cite

In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated using uncertainty-based weights in the optimization process. Previous methods rely on sampled ensembles, which do not capture all aspects of uncertainty. We provide a systematic analysis of the sources of uncertainty in the noisy supervision that occurs in RL, and introduce inverse-variance RL, a Bayesian framework which combines probabilistic ensembles and Batch Inverse Variance weighting. We propose a method whereby two complementary uncertainty estimation methods account for both the Q-value and the environment stochasticity to better mitigate the negative impacts of noisy supervision. Our results show significant improvement in terms of sample efficiency on discrete and continuous control tasks.

show abstract

“…This is mainly due to its large network capacity. Training such large models can also potentially introduce negative impact on the environment due to the significant power consumption of computational hardware [31]. This can be mitigated by using more efficient hardware, and through model complexity reduction based on network compression [9] and knowledge distillation [22].…”

Section: Limitations and Potential Negative Impactsmentioning

confidence: 99%

ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

Danier¹,

Zhang²,

Bull³

2021

Preprint

View full text Add to dashboard Cite

Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding. VFI can be extremely challenging, particularly in sequences containing large motions, occlusions or dynamic textures, where existing approaches fail to offer perceptually robust interpolation performance. In this context, we present a novel deep learning based VFI method, ST-MFNet, based on a Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale multi-flow predictor to estimate many-to-one intermediate flows, which are combined with conventional one-to-one optical flows to capture both large and complex motions. In order to enhance interpolation performance for various textures, a 3D CNN is also employed to model the content dynamics over an extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN framework, which was originally developed for texture synthesis, with the aim of further improving perceptual interpolation quality. Our approach has been comprehensively evaluated -compared with fourteen state-of-the-art VFI algorithms -clearly demonstrating that ST-MFNet consistently outperforms these benchmarks on varied and representative test datasets, with significant gains up to 1.09dB in PSNR for cases including large motions and dynamic textures. Project page: https://danielism97.github.io/ST-MFNet.

show abstract

Quantifying the Carbon Emissions of Machine Learning

Cited by 127 publications

References 14 publications

Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost

Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost

Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation

ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

Contact Info

Product

Resources

About