436 research outputs found
Sparse Training Theory for Scalable and Efficient Agents:Blue Sky Ideas Track
A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning. Deep Neural
Networks have proven to cope perfectly with all learning paradigms, i.e.
supervised, unsupervised, and reinforcement learning. Nevertheless, traditional
deep learning approaches make use of cloud computing facilities and do not
scale well to autonomous agents with low computational resources. Even in the
cloud, they suffer from computational and memory limitations, and they cannot
be used to model adequately large physical worlds for agents which assume
networks with billions of neurons. These issues are addressed in the last few
years by the emerging topic of sparse training, which trains sparse networks
from scratch. This paper discusses sparse training state-of-the-art, its
challenges and limitations while introducing a couple of new theoretical
research directions which has the potential of alleviating sparse training
limitations to push deep learning scalability well beyond its current
boundaries. Nevertheless, the theoretical advancements impact in complex
multi-agents settings is discussed from a real-world perspective, using the
smart grid case study
A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives
Graph-related applications have experienced significant growth in academia
and industry, driven by the powerful representation capabilities of graph.
However, efficiently executing these applications faces various challenges,
such as load imbalance, random memory access, etc. To address these challenges,
researchers have proposed various acceleration systems, including software
frameworks and hardware accelerators, all of which incorporate graph
pre-processing (GPP). GPP serves as a preparatory step before the formal
execution of applications, involving techniques such as sampling, reorder, etc.
However, GPP execution often remains overlooked, as the primary focus is
directed towards enhancing graph applications themselves. This oversight is
concerning, especially considering the explosive growth of real-world graph
data, where GPP becomes essential and even dominates system running overhead.
Furthermore, GPP methods exhibit significant variations across devices and
applications due to high customization. Unfortunately, no comprehensive work
systematically summarizes GPP. To address this gap and foster a better
understanding of GPP, we present a comprehensive survey dedicated to this area.
We propose a double-level taxonomy of GPP, considering both algorithmic and
hardware perspectives. Through listing relavent works, we illustrate our
taxonomy and conduct a thorough analysis and summary of diverse GPP techniques.
Lastly, we discuss challenges in GPP and potential future directions
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