4 papers
Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs
Changhai Man, Joongun Park, Hanjiang Wu +3
Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed worklo…
MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
Srinivas Sridharan, Theodor-Adrian Badea, Andy Balogh +26
The fast pace of artificial intelligence~(AI) innovation demands an agile methodology for observation, reproduction and optimization of distributed machine learning~(ML) workload b…
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…
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…