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Sustainable ai: Environmental implications, challenges and opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …
Aligning artificial intelligence with climate change mitigation
There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Federated learning with buffered asynchronous aggregation
Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Federated Learning (FL) has been gaining significant traction across different ML tasks,
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …
ranging from vision to keyboard predictions. In large-scale deployments, client heterogeneity …
Edge intelligence: Paving the last mile of artificial intelligence with edge computing
With the breakthroughs in deep learning, the recent years have witnessed a booming of
artificial intelligence (AI) applications and services, spanning from personal assistant to …
artificial intelligence (AI) applications and services, spanning from personal assistant to …
Applications of artificial intelligence and machine learning algorithms to crystallization
Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …
variety of scientific applications and cutting-edge technologies, where they have a …
SPINN: Synergistic progressive inference of neural networks over device and cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …
uniformly sustaining high-performance inference on mobile has been elusive due to the …
Serving {DNNs} like clockwork: Performance predictability from the bottom up
Machine learning inference is becoming a core building block for interactive web
applications. As a result, the underlying model serving systems on which these applications …
applications. As a result, the underlying model serving systems on which these applications …
Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology
Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …
academia, but a standard process model to improve success and efficiency of machine …