First Keynotes
TITLE: Tensor Networks in the Era of Sustainable AI
Abstract: As AI models become increasingly complex and data-intensive, the demand
for computational resources escalates, raising concerns about the carbon
footprint and energy efficiency of these technologies. This talk first
introduces the concept and diagrams of tensor networks, then discusses how
tensor networks, a mathematical framework originally developed in the context
of quantum physics, offer a promising pathway to mitigate these challenges. By
enabling the efficient representation and manipulation of high-dimensional
data, tensor networks significantly reduce the computational cost and energy
consumption of large-scale AI operations. The presentation will cover
foundational concepts of tensor networks, including their structure,
properties, and how they facilitate sparse data representation and dimensional
reduction. It will also highlight recent advancements in integrating tensor
networks with deep learning models, demonstrating their effectiveness in tasks
such as image recognition, action recognition, natural language processing,
with a fraction of the computational resources typically required. A special
focus will be directed towards the application of tensor networks in the
optimization of large language models, illustrating their substantial impact in
this specific area. Finally, it concludes with discussion on the future
directions and potential challenges in this emerging field.
Biography: Dr. Zenglin Xu earned his Ph.D. in Computer Science and
Engineering from the Chinese University of Hong Kong. He holds the position of
full Professor at the Harbin Institute of Technology, Shenzhen, and is also affiliated
with Peng Cheng Laboratory. Dr. Xu's academic journey has led him through
several renowned institutions worldwide. He has been affiliated with Michigan
State University, Saarland University's Cluster of Excellence, the Max Planck
Institute for Informatics, Purdue University, and the University of Electronic
Science and Technology of China. With a primary focus on machine learning, Dr.
Xu's research extends into its applications in fields such as computer vision,
health informatics, and natural language processing. He has published over 180
peer-reviewed articles in leading journals and conferences within the
artificial intelligence domain. His dedication and excellence in the realm of
research have been recognized multiple times. Notably, he was honored with the
Outstanding Student Paper Mention at AAAI 2015, the Best Student Paper
Runner-Up at ACML 2016, and the 2016 Young Researcher Award presented by APNNS.
In addition to his research contributions, Dr. Xu plays an active role in the
academic community. He serves as an Associate Editor for Neural Networks and
consistently contributes as an Area Chair or Senior Program Committee member
for conferences including ACL, EMNLP, AAAI, and IJCAI. Currently, he holds the
position of Vice President for Education at the International Neural Network
Society (INNS).
Second Keynotes
TITLE: Kapacity - An Open Source Solution for Green
Data Centers with Predictive AutoScaling
Abstract: The exponential growth of cloud computing has led to a disturbing escalation in carbon emissions from data centers, which now contribute to over 3% of global greenhouse gas emissions. This pressing issue calls for urgent action to mitigate their escalating impact on the environment, as well as the strain they impose on the global climate. As part of the efforts towards Ant Group's goal towards carbon neutrality by 2030 and the general ESG (Environmental, Social and Governance) strategy, we focus on improving resource utilization in order to save electricity usage of data centers.
In this talk, both the engineering and algorithmic
backgrounds of Ant Group's autoscaling infrastructure (Kapacity) will be
introduced. Our proposed methodologies, such as Full Scaling Automation
(FSA), NeuralReconciler for Hierarchical Time Series and Structured Learning
and Task-based Optimization for Time Series Forecasting on Hierarchies (SLOTH),
not only forecast the resource demands effectively, but can also dynamically
adapt resources to accommodate changing workloads in large-scale cloud computing
clusters, enabling the clusters in data centers to maintain their desired CPU
utilization target and thus improve energy efficiency. Our approaches achieve
significant performance improvement compared to the existing work in real-world
datasets. These methods have been deployed on large-scale cloud computing
clusters in industrial data centers, and according to the certification of the
China Environmental United Certification Center (CEC), a reduction of 947 tons
of carbon dioxide, equivalent to a saving of 1538,000 kWh of electricity, was
achieved during the Double 11 shopping festival of 2022, marking a critical
step for our company’s strategic goal towards carbon neutrality.
Our proposed systems been running robustly and continuously since then, contributing to the energy efficiency of Ant Group's cloud services. We have further open-sourced Kapacity, which is used for Ant Group's production data centers, and we remain committed to continuously innovating and developing new solutions to contribute towards the creation of a more sustainable technological infrastructure.
Biography: Dr. James Zhang is the Managing
Director of AI Forecasting and Strategy Platform of Ant Group. Dr. Zhang
obtained his Ph.D. degree from Univ. of Ottawa, Canada in Electrical
Engineering, and both his Master’s and Bachelor degrees from Zhejiang Univ.,
China. Before he joined Ant Financial, Dr. Zhang worked on finance-related AI
at Bloomberg, helped setting up the AI branch of Bloomberg Labs and initiating
the GPU computation farm of Bloomberg. Dr. Zhang worked in various areas
including image processing, natural language processing, high-speed hardware
development, optical networks, operations research, biometrics, and financial systems.