4 citations · 5 across the 3 of their papers we have counts for
Showing cs.DCShow all
3 papers · 1 filter
cs.DC2026
Exceeding the Numerical and Performance Characteristics of IEEE-754 SGEMM with BFloat16 Tensor Cores on GPUs for Scientific Computing
Harun Bayraktar, Cole Brower, John Gunnels +9
Largely due to their increased native capacity for numerical intensity and power efficiency, reduced-precision floating-point computing resources, primarily used in artificial inte…
cs.DC2021
Arithmetic-Intensity-Guided Fault Tolerance for Neural Network Inference on GPUs
Jack Kosaian, K. V. Rashmi
Neural networks (NNs) are increasingly employed in safety-critical domains and in environments prone to unreliability (e.g., soft errors), such as on spacecraft. Therefore, it is c…
cs.DC2019
Parity Models: A General Framework for Coding-Based Resilience in ML Inference
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Machine learning models are becoming the primary workhorses for many applications. Production services deploy models through prediction serving systems that take in queries and ret…