3 papers
cs.DC2025
Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go, Joongun Park, Spandan More +5
The rapid scaling of Large Language Models (LLMs) has pushed training workloads far beyond the limits of single-node analysis, demanding a deeper understanding of how these models…
cs.AR2025
NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
Hanchen Yang, Zishen Wan, Ritik Raj +5
Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of…
cs.DC2024
Enhancing Scalability and Performance in Influence Maximization with Optimized Parallel Processing
Hanjiang Wu, Huan Xu, Joongun Park +5
Influence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employe…