most citedEnhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms

2 citations · 2 across the 2 of their papers we have counts for

collaborators

3 papers

cs.LG2026★ 2 cited

Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms

Sheila Schoepp, Mehran Taghian, Shotaro Miwa +3

Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplica…

cs.LG2026

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

Mehran Taghian, Yunke Peng, Xing Huang +22

Large foundation models have become central to modern machine learning, with performance scaling predictably with model size and data. However, training and deploying such models i…

cs.LG2026

HiFloat4 Format for Language Model Inference

Yuanyong Luo, Jing Huang, Yu Cheng +19

This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata…