2 papers
cs.LG2025
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization
Jiayi Tian, Jinming Lu, Hai Li +4
Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrai…
cs.AR2024
Exploring and Exploiting Runtime Reconfigurable Floating Point Precision in Scientific Computing: a Case Study for Solving PDEs
Cong "Callie" Hao
Scientific computing applications, such as computational fluid dynamics and climate modeling, typically rely on 64-bit double-precision floating-point operations, which are extreme…